id	sid	tid	token	lemma	pos
fcis-19688	1	1	frontiers	frontier	NOUN
fcis-19688	1	2	in	in	ADP
fcis-19688	1	3	computing	computing	NOUN
fcis-19688	1	4	and	and	CCONJ
fcis-19688	1	5	intelligent	intelligent	ADJ
fcis-19688	1	6	systems	system	NOUN
fcis-19688	1	7	issn	issn	VERB
fcis-19688	1	8	:	:	PUNCT
fcis-19688	1	9	2832	2832	NUM
fcis-19688	1	10	-	-	SYM
fcis-19688	1	11	6024	6024	NUM
fcis-19688	1	12	|	|	NOUN
fcis-19688	1	13	vol	vol	NOUN
fcis-19688	1	14	.	.	PROPN
fcis-19688	2	1	7	7	NUM
fcis-19688	2	2	,	,	PUNCT
fcis-19688	2	3	no	no	INTJ
fcis-19688	2	4	.	.	NOUN
fcis-19688	2	5	3	3	NUM
fcis-19688	2	6	,	,	PUNCT
fcis-19688	2	7	2024	2024	NUM
fcis-19688	2	8	67	67	NUM
fcis-19688	2	9	optimization	optimization	NOUN
fcis-19688	2	10	and	and	CCONJ
fcis-19688	2	11	performance	performance	NOUN
fcis-19688	2	12	evaluation	evaluation	NOUN
fcis-19688	2	13	of	of	ADP
fcis-19688	2	14	deep	deep	ADJ
fcis-19688	2	15	learning	learning	NOUN
fcis-19688	2	16	algorithm	algorithm	NOUN
fcis-19688	2	17	in	in	ADP
fcis-19688	2	18	medical	medical	ADJ
fcis-19688	2	19	image	image	NOUN
fcis-19688	2	20	processing	process	VERB
fcis-19688	2	21	jingbo	jingbo	PROPN
fcis-19688	2	22	zhang	zhang	PROPN
fcis-19688	2	23	1	1	NUM
fcis-19688	2	24	,	,	PUNCT
fcis-19688	2	25	lingxi	lingxi	NOUN
fcis-19688	2	26	xiao	xiao	PROPN
fcis-19688	2	27	2	2	NUM
fcis-19688	2	28	,	,	PUNCT
fcis-19688	2	29	yuwei	yuwei	NOUN
fcis-19688	2	30	zhang	zhang	PROPN
fcis-19688	2	31	3	3	NUM
fcis-19688	2	32	,	,	PUNCT
fcis-19688	2	33	jiatao	jiatao	PROPN
fcis-19688	2	34	lai	lai	PROPN
fcis-19688	2	35	4	4	NUM
fcis-19688	2	36	,	,	PUNCT
fcis-19688	2	37	yutian	yutian	ADJ
fcis-19688	2	38	yang	yang	PROPN
fcis-19688	2	39	5	5	NUM
fcis-19688	2	40	1	1	NUM
fcis-19688	2	41	carnegie	carnegie	PROPN
fcis-19688	2	42	mellon	mellon	PROPN
fcis-19688	2	43	university	university	PROPN
fcis-19688	2	44	,	,	PUNCT
fcis-19688	2	45	arlington	arlington	PROPN
fcis-19688	2	46	,	,	PUNCT
fcis-19688	2	47	usa	usa	PROPN
fcis-19688	2	48	2	2	NUM
fcis-19688	2	49	georgia	georgia	PROPN
fcis-19688	2	50	institute	institute	PROPN
fcis-19688	2	51	of	of	ADP
fcis-19688	2	52	technology	technology	PROPN
fcis-19688	2	53	,	,	PUNCT
fcis-19688	2	54	atlanta	atlanta	PROPN
fcis-19688	2	55	,	,	PUNCT
fcis-19688	2	56	usa	usa	PROPN
fcis-19688	2	57	3	3	NUM
fcis-19688	2	58	duke	duke	PROPN
fcis-19688	2	59	university	university	PROPN
fcis-19688	2	60	,	,	PUNCT
fcis-19688	2	61	durham	durham	PROPN
fcis-19688	2	62	,	,	PUNCT
fcis-19688	2	63	usa	usa	PROPN
fcis-19688	2	64	4	4	NUM
fcis-19688	2	65	the	the	DET
fcis-19688	2	66	university	university	PROPN
fcis-19688	2	67	of	of	ADP
fcis-19688	2	68	texas	texas	PROPN
fcis-19688	2	69	at	at	ADP
fcis-19688	2	70	dallas	dallas	PROPN
fcis-19688	2	71	,	,	PUNCT
fcis-19688	2	72	richardson	richardson	PROPN
fcis-19688	2	73	,	,	PUNCT
fcis-19688	2	74	usa	usa	PROPN
fcis-19688	2	75	5	5	NUM
fcis-19688	2	76	university	university	PROPN
fcis-19688	2	77	of	of	ADP
fcis-19688	2	78	califomia	califomia	PROPN
fcis-19688	2	79	,	,	PUNCT
fcis-19688	2	80	davis	davis	PROPN
fcis-19688	2	81	,	,	PUNCT
fcis-19688	2	82	davis	davis	PROPN
fcis-19688	2	83	,	,	PUNCT
fcis-19688	2	84	usa	usa	PROPN
fcis-19688	2	85	abstract	abstract	PROPN
fcis-19688	2	86	:	:	PUNCT
fcis-19688	2	87	in	in	ADP
fcis-19688	2	88	this	this	DET
fcis-19688	2	89	paper	paper	NOUN
fcis-19688	2	90	,	,	PUNCT
fcis-19688	2	91	the	the	DET
fcis-19688	2	92	optimization	optimization	NOUN
fcis-19688	2	93	and	and	CCONJ
fcis-19688	2	94	performance	performance	NOUN
fcis-19688	2	95	evaluation	evaluation	NOUN
fcis-19688	2	96	of	of	ADP
fcis-19688	2	97	deep	deep	ADJ
fcis-19688	2	98	learning	learning	NOUN
fcis-19688	2	99	algorithm	algorithm	NOUN
fcis-19688	2	100	in	in	ADP
fcis-19688	2	101	medical	medical	ADJ
fcis-19688	2	102	image	image	NOUN
fcis-19688	2	103	processing	processing	NOUN
fcis-19688	2	104	are	be	AUX
fcis-19688	2	105	studied	study	VERB
fcis-19688	2	106	.	.	PUNCT
fcis-19688	3	1	firstly	firstly	ADV
fcis-19688	3	2	,	,	PUNCT
fcis-19688	3	3	the	the	DET
fcis-19688	3	4	paper	paper	NOUN
fcis-19688	3	5	introduces	introduce	VERB
fcis-19688	3	6	the	the	DET
fcis-19688	3	7	importance	importance	NOUN
fcis-19688	3	8	and	and	CCONJ
fcis-19688	3	9	challenges	challenge	NOUN
fcis-19688	3	10	of	of	ADP
fcis-19688	3	11	medical	medical	ADJ
fcis-19688	3	12	image	image	NOUN
fcis-19688	3	13	processing	processing	NOUN
fcis-19688	3	14	,	,	PUNCT
fcis-19688	3	15	and	and	CCONJ
fcis-19688	3	16	expounds	expound	VERB
fcis-19688	3	17	the	the	DET
fcis-19688	3	18	application	application	NOUN
fcis-19688	3	19	prospect	prospect	NOUN
fcis-19688	3	20	of	of	ADP
fcis-19688	3	21	deep	deep	ADJ
fcis-19688	3	22	learning	learning	NOUN
fcis-19688	3	23	in	in	ADP
fcis-19688	3	24	this	this	DET
fcis-19688	3	25	field	field	NOUN
fcis-19688	3	26	.	.	PUNCT
fcis-19688	4	1	subsequently	subsequently	ADV
fcis-19688	4	2	,	,	PUNCT
fcis-19688	4	3	this	this	DET
fcis-19688	4	4	paper	paper	NOUN
fcis-19688	4	5	discusses	discuss	VERB
fcis-19688	4	6	the	the	DET
fcis-19688	4	7	optimization	optimization	NOUN
fcis-19688	4	8	methods	method	NOUN
fcis-19688	4	9	of	of	ADP
fcis-19688	4	10	deep	deep	ADJ
fcis-19688	4	11	learning	learning	NOUN
fcis-19688	4	12	algorithm	algorithm	NOUN
fcis-19688	4	13	in	in	ADP
fcis-19688	4	14	detail	detail	NOUN
fcis-19688	4	15	,	,	PUNCT
fcis-19688	4	16	including	include	VERB
fcis-19688	4	17	model	model	NOUN
fcis-19688	4	18	structure	structure	NOUN
fcis-19688	4	19	design	design	NOUN
fcis-19688	4	20	,	,	PUNCT
fcis-19688	4	21	data	datum	NOUN
fcis-19688	4	22	preprocessing	preprocessing	NOUN
fcis-19688	4	23	,	,	PUNCT
fcis-19688	4	24	super	super	ADJ
fcis-19688	4	25	parameter	parameter	NOUN
fcis-19688	4	26	adjustment	adjustment	NOUN
fcis-19688	4	27	and	and	CCONJ
fcis-19688	4	28	so	so	ADV
fcis-19688	4	29	on	on	ADV
fcis-19688	4	30	.	.	PUNCT
fcis-19688	5	1	in	in	ADP
fcis-19688	5	2	terms	term	NOUN
fcis-19688	5	3	of	of	ADP
fcis-19688	5	4	performance	performance	NOUN
fcis-19688	5	5	evaluation	evaluation	NOUN
fcis-19688	5	6	,	,	PUNCT
fcis-19688	5	7	this	this	DET
fcis-19688	5	8	study	study	NOUN
fcis-19688	5	9	selected	select	VERB
fcis-19688	5	10	classic	classic	ADJ
fcis-19688	5	11	models	model	NOUN
fcis-19688	5	12	such	such	ADJ
fcis-19688	5	13	as	as	ADP
fcis-19688	5	14	u	u	NOUN
fcis-19688	5	15	-	-	NOUN
fcis-19688	5	16	net	net	ADJ
fcis-19688	5	17	,	,	PUNCT
fcis-19688	5	18	deeplab	deeplab	NOUN
fcis-19688	5	19	and	and	CCONJ
fcis-19688	5	20	densenet	densenet	NOUN
fcis-19688	5	21	,	,	PUNCT
fcis-19688	5	22	and	and	CCONJ
fcis-19688	5	23	compared	compare	VERB
fcis-19688	5	24	them	they	PRON
fcis-19688	5	25	with	with	ADP
fcis-19688	5	26	roc	roc	PROPN
fcis-19688	5	27	curve	curve	NOUN
fcis-19688	5	28	and	and	CCONJ
fcis-19688	5	29	auc	auc	NOUN
fcis-19688	5	30	value	value	NOUN
fcis-19688	5	31	to	to	PART
fcis-19688	5	32	evaluate	evaluate	VERB
fcis-19688	5	33	their	their	PRON
fcis-19688	5	34	predictive	predictive	ADJ
fcis-19688	5	35	ability	ability	NOUN
fcis-19688	5	36	in	in	ADP
fcis-19688	5	37	medical	medical	ADJ
fcis-19688	5	38	image	image	NOUN
fcis-19688	5	39	classification	classification	NOUN
fcis-19688	5	40	.	.	PUNCT
fcis-19688	6	1	the	the	DET
fcis-19688	6	2	results	result	NOUN
fcis-19688	6	3	show	show	VERB
fcis-19688	6	4	that	that	SCONJ
fcis-19688	6	5	the	the	DET
fcis-19688	6	6	densenet	densenet	NOUN
fcis-19688	6	7	model	model	NOUN
fcis-19688	6	8	shows	show	VERB
fcis-19688	6	9	high	high	ADJ
fcis-19688	6	10	performance	performance	NOUN
fcis-19688	6	11	in	in	ADP
fcis-19688	6	12	prediction	prediction	NOUN
fcis-19688	6	13	accuracy	accuracy	NOUN
fcis-19688	6	14	,	,	PUNCT
fcis-19688	6	15	while	while	SCONJ
fcis-19688	6	16	the	the	DET
fcis-19688	6	17	performance	performance	NOUN
fcis-19688	6	18	of	of	ADP
fcis-19688	6	19	u	u	NOUN
fcis-19688	6	20	-	-	NOUN
fcis-19688	6	21	net	net	ADJ
fcis-19688	6	22	and	and	CCONJ
fcis-19688	6	23	deeplab	deeplab	NOUN
fcis-19688	6	24	models	model	NOUN
fcis-19688	6	25	is	be	AUX
fcis-19688	6	26	slightly	slightly	ADV
fcis-19688	6	27	average	average	ADJ
fcis-19688	6	28	.	.	PUNCT
fcis-19688	7	1	finally	finally	ADV
fcis-19688	7	2	,	,	PUNCT
fcis-19688	7	3	the	the	DET
fcis-19688	7	4	advantages	advantage	NOUN
fcis-19688	7	5	and	and	CCONJ
fcis-19688	7	6	disadvantages	disadvantage	NOUN
fcis-19688	7	7	of	of	ADP
fcis-19688	7	8	each	each	DET
fcis-19688	7	9	model	model	NOUN
fcis-19688	7	10	are	be	AUX
fcis-19688	7	11	analyzed	analyze	VERB
fcis-19688	7	12	,	,	PUNCT
fcis-19688	7	13	and	and	CCONJ
fcis-19688	7	14	the	the	DET
fcis-19688	7	15	future	future	ADJ
fcis-19688	7	16	research	research	NOUN
fcis-19688	7	17	direction	direction	NOUN
fcis-19688	7	18	is	be	AUX
fcis-19688	7	19	prospected	prospect	VERB
fcis-19688	7	20	.	.	PUNCT
fcis-19688	8	1	this	this	DET
fcis-19688	8	2	study	study	NOUN
fcis-19688	8	3	is	be	AUX
fcis-19688	8	4	of	of	ADP
fcis-19688	8	5	great	great	ADJ
fcis-19688	8	6	significance	significance	NOUN
fcis-19688	8	7	to	to	PART
fcis-19688	8	8	promote	promote	VERB
fcis-19688	8	9	the	the	DET
fcis-19688	8	10	development	development	NOUN
fcis-19688	8	11	and	and	CCONJ
fcis-19688	8	12	application	application	NOUN
fcis-19688	8	13	of	of	ADP
fcis-19688	8	14	medical	medical	ADJ
fcis-19688	8	15	image	image	NOUN
fcis-19688	8	16	processing	processing	NOUN
fcis-19688	8	17	technology	technology	NOUN
fcis-19688	8	18	,	,	PUNCT
fcis-19688	8	19	and	and	CCONJ
fcis-19688	8	20	provides	provide	VERB
fcis-19688	8	21	important	important	ADJ
fcis-19688	8	22	theoretical	theoretical	ADJ
fcis-19688	8	23	and	and	CCONJ
fcis-19688	8	24	technical	technical	ADJ
fcis-19688	8	25	support	support	NOUN
fcis-19688	8	26	for	for	ADP
fcis-19688	8	27	medical	medical	ADJ
fcis-19688	8	28	diagnosis	diagnosis	NOUN
fcis-19688	8	29	and	and	CCONJ
fcis-19688	8	30	treatment	treatment	NOUN
fcis-19688	8	31	.	.	PUNCT
fcis-19688	9	1	keywords	keyword	NOUN
fcis-19688	9	2	:	:	PUNCT
fcis-19688	9	3	deep	deep	ADJ
fcis-19688	9	4	learning	learning	NOUN
fcis-19688	9	5	;	;	PUNCT
fcis-19688	9	6	performance	performance	NOUN
fcis-19688	9	7	evaluation	evaluation	NOUN
fcis-19688	9	8	;	;	PUNCT
fcis-19688	9	9	medical	medical	ADJ
fcis-19688	9	10	image	image	NOUN
fcis-19688	9	11	.	.	PUNCT
fcis-19688	10	1	1	1	X
fcis-19688	10	2	.	.	X
fcis-19688	10	3	introduction	introduction	NOUN
fcis-19688	10	4	medical	medical	ADJ
fcis-19688	10	5	image	image	NOUN
fcis-19688	10	6	processing	processing	NOUN
fcis-19688	10	7	is	be	AUX
fcis-19688	10	8	one	one	NUM
fcis-19688	10	9	of	of	ADP
fcis-19688	10	10	the	the	DET
fcis-19688	10	11	important	important	ADJ
fcis-19688	10	12	branches	branch	NOUN
fcis-19688	10	13	in	in	ADP
fcis-19688	10	14	the	the	DET
fcis-19688	10	15	medical	medical	ADJ
fcis-19688	10	16	field	field	NOUN
fcis-19688	10	17	,	,	PUNCT
fcis-19688	10	18	which	which	PRON
fcis-19688	10	19	covers	cover	VERB
fcis-19688	10	20	many	many	ADJ
fcis-19688	10	21	aspects	aspect	NOUN
fcis-19688	10	22	such	such	ADJ
fcis-19688	10	23	as	as	ADP
fcis-19688	10	24	extracting	extract	VERB
fcis-19688	10	25	information	information	NOUN
fcis-19688	10	26	from	from	ADP
fcis-19688	10	27	medical	medical	ADJ
fcis-19688	10	28	image	image	NOUN
fcis-19688	10	29	data	datum	NOUN
fcis-19688	10	30	,	,	PUNCT
fcis-19688	10	31	diagnosing	diagnose	VERB
fcis-19688	10	32	diseases	disease	NOUN
fcis-19688	10	33	and	and	CCONJ
fcis-19688	10	34	assisting	assist	VERB
fcis-19688	10	35	doctors	doctor	NOUN
fcis-19688	10	36	in	in	ADP
fcis-19688	10	37	decision	decision	NOUN
fcis-19688	10	38	-	-	PUNCT
fcis-19688	10	39	making	making	NOUN
fcis-19688	10	40	.	.	PUNCT
fcis-19688	11	1	with	with	ADP
fcis-19688	11	2	the	the	DET
fcis-19688	11	3	rapid	rapid	ADJ
fcis-19688	11	4	development	development	NOUN
fcis-19688	11	5	of	of	ADP
fcis-19688	11	6	deep	deep	ADJ
fcis-19688	11	7	learning	learning	NOUN
fcis-19688	11	8	technology	technology	NOUN
fcis-19688	11	9	,	,	PUNCT
fcis-19688	11	10	deep	deep	ADJ
fcis-19688	11	11	learning	learning	NOUN
fcis-19688	11	12	algorithm	algorithm	NOUN
fcis-19688	11	13	has	have	AUX
fcis-19688	11	14	shown	show	VERB
fcis-19688	11	15	great	great	ADJ
fcis-19688	11	16	potential	potential	ADJ
fcis-19688	11	17	and	and	CCONJ
fcis-19688	11	18	application	application	NOUN
fcis-19688	11	19	prospect	prospect	NOUN
fcis-19688	11	20	in	in	ADP
fcis-19688	11	21	medical	medical	ADJ
fcis-19688	11	22	image	image	NOUN
fcis-19688	11	23	processing	processing	NOUN
fcis-19688	11	24	.	.	PUNCT
fcis-19688	12	1	compared	compare	VERB
fcis-19688	12	2	with	with	ADP
fcis-19688	12	3	the	the	DET
fcis-19688	12	4	traditional	traditional	ADJ
fcis-19688	12	5	image	image	NOUN
fcis-19688	12	6	processing	processing	NOUN
fcis-19688	12	7	methods	method	NOUN
fcis-19688	12	8	,	,	PUNCT
fcis-19688	12	9	the	the	DET
fcis-19688	12	10	deep	deep	ADJ
fcis-19688	12	11	learning	learning	NOUN
fcis-19688	12	12	algorithm	algorithm	NOUN
fcis-19688	12	13	can	can	AUX
fcis-19688	12	14	realize	realize	VERB
fcis-19688	12	15	more	more	ADV
fcis-19688	12	16	accurate	accurate	ADJ
fcis-19688	12	17	medical	medical	ADJ
fcis-19688	12	18	image	image	NOUN
fcis-19688	12	19	analysis	analysis	NOUN
fcis-19688	12	20	and	and	CCONJ
fcis-19688	12	21	diagnosis	diagnosis	NOUN
fcis-19688	12	22	by	by	ADP
fcis-19688	12	23	learning	learn	VERB
fcis-19688	12	24	the	the	DET
fcis-19688	12	25	features	feature	NOUN
fcis-19688	12	26	in	in	ADP
fcis-19688	12	27	large	large	ADJ
fcis-19688	12	28	-	-	PUNCT
fcis-19688	12	29	scale	scale	NOUN
fcis-19688	12	30	data	datum	NOUN
fcis-19688	12	31	,	,	PUNCT
fcis-19688	12	32	which	which	PRON
fcis-19688	12	33	provides	provide	VERB
fcis-19688	12	34	a	a	DET
fcis-19688	12	35	more	more	ADV
fcis-19688	12	36	accurate	accurate	ADJ
fcis-19688	12	37	and	and	CCONJ
fcis-19688	12	38	faster	fast	ADJ
fcis-19688	12	39	solution	solution	NOUN
fcis-19688	12	40	for	for	ADP
fcis-19688	12	41	medical	medical	ADJ
fcis-19688	12	42	diagnosis	diagnosis	NOUN
fcis-19688	12	43	.	.	PUNCT
fcis-19688	13	1	however	however	ADV
fcis-19688	13	2	,	,	PUNCT
fcis-19688	13	3	although	although	SCONJ
fcis-19688	13	4	deep	deep	ADJ
fcis-19688	13	5	learning	learning	NOUN
fcis-19688	13	6	has	have	AUX
fcis-19688	13	7	shown	show	VERB
fcis-19688	13	8	great	great	ADJ
fcis-19688	13	9	potential	potential	NOUN
fcis-19688	13	10	in	in	ADP
fcis-19688	13	11	medical	medical	ADJ
fcis-19688	13	12	image	image	NOUN
fcis-19688	13	13	processing	processing	NOUN
fcis-19688	13	14	,	,	PUNCT
fcis-19688	13	15	its	its	PRON
fcis-19688	13	16	application	application	NOUN
fcis-19688	13	17	still	still	ADV
fcis-19688	13	18	faces	face	VERB
fcis-19688	13	19	many	many	ADJ
fcis-19688	13	20	challenges	challenge	NOUN
fcis-19688	13	21	and	and	CCONJ
fcis-19688	13	22	limitations	limitation	NOUN
fcis-19688	13	23	.	.	PUNCT
fcis-19688	14	1	first	first	ADV
fcis-19688	14	2	,	,	PUNCT
fcis-19688	14	3	the	the	DET
fcis-19688	14	4	particularity	particularity	NOUN
fcis-19688	14	5	of	of	ADP
fcis-19688	14	6	medical	medical	ADJ
fcis-19688	14	7	image	image	NOUN
fcis-19688	14	8	data	datum	NOUN
fcis-19688	14	9	and	and	CCONJ
fcis-19688	14	10	the	the	DET
fcis-19688	14	11	clinical	clinical	ADJ
fcis-19688	14	12	application	application	NOUN
fcis-19688	14	13	demand	demand	NOUN
fcis-19688	14	14	in	in	ADP
fcis-19688	14	15	medical	medical	ADJ
fcis-19688	14	16	field	field	NOUN
fcis-19688	14	17	put	put	VERB
fcis-19688	14	18	forward	forward	ADV
fcis-19688	14	19	higher	high	ADJ
fcis-19688	14	20	requirements	requirement	NOUN
fcis-19688	14	21	for	for	ADP
fcis-19688	14	22	the	the	DET
fcis-19688	14	23	robustness	robustness	NOUN
fcis-19688	14	24	and	and	CCONJ
fcis-19688	14	25	interpretability	interpretability	NOUN
fcis-19688	14	26	of	of	ADP
fcis-19688	14	27	the	the	DET
fcis-19688	14	28	algorithm	algorithm	NOUN
fcis-19688	14	29	[	[	X
fcis-19688	14	30	1	1	NUM
fcis-19688	14	31	]	]	PUNCT
fcis-19688	14	32	.	.	PUNCT
fcis-19688	15	1	secondly	secondly	ADV
fcis-19688	15	2	,	,	PUNCT
fcis-19688	15	3	medical	medical	ADJ
fcis-19688	15	4	image	image	NOUN
fcis-19688	15	5	data	datum	NOUN
fcis-19688	15	6	are	be	AUX
fcis-19688	15	7	often	often	ADV
fcis-19688	15	8	scarce	scarce	ADJ
fcis-19688	15	9	and	and	CCONJ
fcis-19688	15	10	the	the	DET
fcis-19688	15	11	labeling	labeling	NOUN
fcis-19688	15	12	cost	cost	NOUN
fcis-19688	15	13	is	be	AUX
fcis-19688	15	14	high	high	ADJ
fcis-19688	15	15	,	,	PUNCT
fcis-19688	15	16	so	so	ADV
fcis-19688	15	17	how	how	SCONJ
fcis-19688	15	18	to	to	PART
fcis-19688	15	19	effectively	effectively	ADV
fcis-19688	15	20	use	use	VERB
fcis-19688	15	21	limited	limited	ADJ
fcis-19688	15	22	data	data	NOUN
fcis-19688	15	23	resources	resource	NOUN
fcis-19688	15	24	for	for	ADP
fcis-19688	15	25	model	model	NOUN
fcis-19688	15	26	training	training	NOUN
fcis-19688	15	27	has	have	AUX
fcis-19688	15	28	become	become	VERB
fcis-19688	15	29	an	an	DET
fcis-19688	15	30	important	important	ADJ
fcis-19688	15	31	issue	issue	NOUN
fcis-19688	15	32	.	.	PUNCT
fcis-19688	16	1	in	in	ADP
fcis-19688	16	2	addition	addition	NOUN
fcis-19688	16	3	,	,	PUNCT
fcis-19688	16	4	the	the	DET
fcis-19688	16	5	complexity	complexity	NOUN
fcis-19688	16	6	and	and	CCONJ
fcis-19688	16	7	large	large	ADJ
fcis-19688	16	8	amount	amount	NOUN
fcis-19688	16	9	of	of	ADP
fcis-19688	16	10	calculation	calculation	NOUN
fcis-19688	16	11	of	of	ADP
fcis-19688	16	12	the	the	DET
fcis-19688	16	13	deep	deep	ADJ
fcis-19688	16	14	learning	learning	NOUN
fcis-19688	16	15	model	model	NOUN
fcis-19688	16	16	limit	limit	VERB
fcis-19688	16	17	its	its	PRON
fcis-19688	16	18	popularization	popularization	NOUN
fcis-19688	16	19	and	and	CCONJ
fcis-19688	16	20	deployment	deployment	NOUN
fcis-19688	16	21	in	in	ADP
fcis-19688	16	22	practical	practical	ADJ
fcis-19688	16	23	applications	application	NOUN
fcis-19688	16	24	.	.	PUNCT
fcis-19688	17	1	the	the	DET
fcis-19688	17	2	purpose	purpose	NOUN
fcis-19688	17	3	of	of	ADP
fcis-19688	17	4	this	this	DET
fcis-19688	17	5	paper	paper	NOUN
fcis-19688	17	6	is	be	AUX
fcis-19688	17	7	to	to	PART
fcis-19688	17	8	explore	explore	VERB
fcis-19688	17	9	the	the	DET
fcis-19688	17	10	methods	method	NOUN
fcis-19688	17	11	and	and	CCONJ
fcis-19688	17	12	techniques	technique	NOUN
fcis-19688	17	13	of	of	ADP
fcis-19688	17	14	optimization	optimization	NOUN
fcis-19688	17	15	and	and	CCONJ
fcis-19688	17	16	performance	performance	NOUN
fcis-19688	17	17	evaluation	evaluation	NOUN
fcis-19688	17	18	by	by	ADP
fcis-19688	17	19	using	use	VERB
fcis-19688	17	20	deep	deep	ADJ
fcis-19688	17	21	learning	learning	NOUN
fcis-19688	17	22	algorithm	algorithm	NOUN
fcis-19688	17	23	in	in	ADP
fcis-19688	17	24	medical	medical	ADJ
fcis-19688	17	25	image	image	NOUN
fcis-19688	17	26	processing	processing	NOUN
fcis-19688	17	27	.	.	PUNCT
fcis-19688	18	1	this	this	DET
fcis-19688	18	2	paper	paper	NOUN
fcis-19688	18	3	summarizes	summarize	VERB
fcis-19688	18	4	the	the	DET
fcis-19688	18	5	application	application	NOUN
fcis-19688	18	6	status	status	NOUN
fcis-19688	18	7	of	of	ADP
fcis-19688	18	8	existing	exist	VERB
fcis-19688	18	9	deep	deep	ADJ
fcis-19688	18	10	learning	learning	NOUN
fcis-19688	18	11	algorithms	algorithm	NOUN
fcis-19688	18	12	in	in	ADP
fcis-19688	18	13	medical	medical	ADJ
fcis-19688	18	14	image	image	NOUN
fcis-19688	18	15	processing	processing	NOUN
fcis-19688	18	16	,	,	PUNCT
fcis-19688	18	17	and	and	CCONJ
fcis-19688	18	18	puts	put	VERB
fcis-19688	18	19	forward	forward	ADV
fcis-19688	18	20	strategies	strategy	NOUN
fcis-19688	18	21	for	for	ADP
fcis-19688	18	22	performance	performance	NOUN
fcis-19688	18	23	improvement	improvement	NOUN
fcis-19688	18	24	and	and	CCONJ
fcis-19688	18	25	optimization	optimization	NOUN
fcis-19688	18	26	in	in	ADP
fcis-19688	18	27	order	order	NOUN
fcis-19688	18	28	to	to	PART
fcis-19688	18	29	provide	provide	VERB
fcis-19688	18	30	reference	reference	NOUN
fcis-19688	18	31	for	for	ADP
fcis-19688	18	32	further	further	ADJ
fcis-19688	18	33	research	research	NOUN
fcis-19688	18	34	and	and	CCONJ
fcis-19688	18	35	practice	practice	NOUN
fcis-19688	18	36	in	in	ADP
fcis-19688	18	37	the	the	DET
fcis-19688	18	38	field	field	NOUN
fcis-19688	18	39	of	of	ADP
fcis-19688	18	40	medical	medical	ADJ
fcis-19688	18	41	image	image	NOUN
fcis-19688	18	42	processing	processing	NOUN
fcis-19688	18	43	.	.	PUNCT
fcis-19688	19	1	2	2	X
fcis-19688	19	2	.	.	X
fcis-19688	19	3	summary	summary	NOUN
fcis-19688	19	4	of	of	ADP
fcis-19688	19	5	related	related	ADJ
fcis-19688	19	6	work	work	NOUN
fcis-19688	19	7	medical	medical	ADJ
fcis-19688	19	8	image	image	NOUN
fcis-19688	19	9	processing	processing	NOUN
fcis-19688	19	10	is	be	AUX
fcis-19688	19	11	an	an	DET
fcis-19688	19	12	important	important	ADJ
fcis-19688	19	13	branch	branch	NOUN
fcis-19688	19	14	of	of	ADP
fcis-19688	19	15	medical	medical	ADJ
fcis-19688	19	16	field	field	NOUN
fcis-19688	19	17	,	,	PUNCT
fcis-19688	19	18	which	which	PRON
fcis-19688	19	19	plays	play	VERB
fcis-19688	19	20	a	a	DET
fcis-19688	19	21	key	key	ADJ
fcis-19688	19	22	role	role	NOUN
fcis-19688	19	23	in	in	ADP
fcis-19688	19	24	disease	disease	NOUN
fcis-19688	19	25	diagnosis	diagnosis	NOUN
fcis-19688	19	26	,	,	PUNCT
fcis-19688	19	27	treatment	treatment	NOUN
fcis-19688	19	28	planning	planning	NOUN
fcis-19688	19	29	and	and	CCONJ
fcis-19688	19	30	treatment	treatment	NOUN
fcis-19688	19	31	effect	effect	NOUN
fcis-19688	19	32	evaluation	evaluation	NOUN
fcis-19688	19	33	.	.	PUNCT
fcis-19688	20	1	in	in	ADP
fcis-19688	20	2	the	the	DET
fcis-19688	20	3	past	past	ADJ
fcis-19688	20	4	decades	decade	NOUN
fcis-19688	20	5	,	,	PUNCT
fcis-19688	20	6	researchers	researcher	NOUN
fcis-19688	20	7	have	have	AUX
fcis-19688	20	8	put	put	VERB
fcis-19688	20	9	forward	forward	ADV
fcis-19688	20	10	many	many	ADJ
fcis-19688	20	11	traditional	traditional	ADJ
fcis-19688	20	12	medical	medical	ADJ
fcis-19688	20	13	image	image	NOUN
fcis-19688	20	14	processing	processing	NOUN
fcis-19688	20	15	methods	method	NOUN
fcis-19688	20	16	,	,	PUNCT
fcis-19688	20	17	such	such	ADJ
fcis-19688	20	18	as	as	ADP
fcis-19688	20	19	feature	feature	NOUN
fcis-19688	20	20	-	-	PUNCT
fcis-19688	20	21	based	base	VERB
fcis-19688	20	22	engineering	engineering	NOUN
fcis-19688	20	23	methods	method	NOUN
fcis-19688	20	24	and	and	CCONJ
fcis-19688	20	25	machine	machine	NOUN
fcis-19688	20	26	learning	learning	NOUN
fcis-19688	20	27	methods	method	NOUN
fcis-19688	20	28	[	[	X
fcis-19688	20	29	2	2	NUM
fcis-19688	20	30	-	-	SYM
fcis-19688	20	31	3	3	NUM
fcis-19688	20	32	]	]	PUNCT
fcis-19688	20	33	.	.	PUNCT
fcis-19688	21	1	however	however	ADV
fcis-19688	21	2	,	,	PUNCT
fcis-19688	21	3	these	these	DET
fcis-19688	21	4	traditional	traditional	ADJ
fcis-19688	21	5	methods	method	NOUN
fcis-19688	21	6	often	often	ADV
fcis-19688	21	7	rely	rely	VERB
fcis-19688	21	8	on	on	ADP
fcis-19688	21	9	the	the	DET
fcis-19688	21	10	characteristics	characteristic	NOUN
fcis-19688	21	11	of	of	ADP
fcis-19688	21	12	manual	manual	ADJ
fcis-19688	21	13	design	design	NOUN
fcis-19688	21	14	,	,	PUNCT
fcis-19688	21	15	are	be	AUX
fcis-19688	21	16	sensitive	sensitive	ADJ
fcis-19688	21	17	to	to	ADP
fcis-19688	21	18	the	the	DET
fcis-19688	21	19	changes	change	NOUN
fcis-19688	21	20	and	and	CCONJ
fcis-19688	21	21	noise	noise	NOUN
fcis-19688	21	22	of	of	ADP
fcis-19688	21	23	medical	medical	ADJ
fcis-19688	21	24	image	image	NOUN
fcis-19688	21	25	data	datum	NOUN
fcis-19688	21	26	,	,	PUNCT
fcis-19688	21	27	and	and	CCONJ
fcis-19688	21	28	are	be	AUX
fcis-19688	21	29	difficult	difficult	ADJ
fcis-19688	21	30	to	to	PART
fcis-19688	21	31	adapt	adapt	VERB
fcis-19688	21	32	to	to	ADP
fcis-19688	21	33	complex	complex	ADJ
fcis-19688	21	34	medical	medical	ADJ
fcis-19688	21	35	image	image	NOUN
fcis-19688	21	36	scenes	scene	NOUN
fcis-19688	21	37	and	and	CCONJ
fcis-19688	21	38	the	the	DET
fcis-19688	21	39	characteristics	characteristic	NOUN
fcis-19688	21	40	of	of	ADP
fcis-19688	21	41	different	different	ADJ
fcis-19688	21	42	diseases	disease	NOUN
fcis-19688	21	43	.	.	PUNCT
fcis-19688	22	1	with	with	ADP
fcis-19688	22	2	the	the	DET
fcis-19688	22	3	rapid	rapid	ADJ
fcis-19688	22	4	development	development	NOUN
fcis-19688	22	5	of	of	ADP
fcis-19688	22	6	deep	deep	ADJ
fcis-19688	22	7	learning	learning	NOUN
fcis-19688	22	8	technology	technology	NOUN
fcis-19688	22	9	,	,	PUNCT
fcis-19688	22	10	deep	deep	ADJ
fcis-19688	22	11	learning	learning	NOUN
fcis-19688	22	12	algorithm	algorithm	NOUN
fcis-19688	22	13	has	have	AUX
fcis-19688	22	14	gradually	gradually	ADV
fcis-19688	22	15	become	become	VERB
fcis-19688	22	16	the	the	DET
fcis-19688	22	17	mainstream	mainstream	NOUN
fcis-19688	22	18	in	in	ADP
fcis-19688	22	19	medical	medical	ADJ
fcis-19688	22	20	image	image	NOUN
fcis-19688	22	21	processing	processing	NOUN
fcis-19688	22	22	[	[	X
fcis-19688	22	23	4	4	NUM
fcis-19688	22	24	-	-	SYM
fcis-19688	22	25	5	5	NUM
fcis-19688	22	26	]	]	PUNCT
fcis-19688	22	27	.	.	PUNCT
fcis-19688	23	1	by	by	ADP
fcis-19688	23	2	constructing	construct	VERB
fcis-19688	23	3	a	a	DET
fcis-19688	23	4	multi	multi	ADJ
fcis-19688	23	5	-	-	ADJ
fcis-19688	23	6	level	level	ADJ
fcis-19688	23	7	neural	neural	ADJ
fcis-19688	23	8	network	network	NOUN
fcis-19688	23	9	model	model	NOUN
fcis-19688	23	10	,	,	PUNCT
fcis-19688	23	11	the	the	DET
fcis-19688	23	12	deep	deep	ADJ
fcis-19688	23	13	learning	learning	NOUN
fcis-19688	23	14	algorithm	algorithm	NOUN
fcis-19688	23	15	can	can	AUX
fcis-19688	23	16	automatically	automatically	ADV
fcis-19688	23	17	learn	learn	VERB
fcis-19688	23	18	feature	feature	NOUN
fcis-19688	23	19	representation	representation	NOUN
fcis-19688	23	20	from	from	ADP
fcis-19688	23	21	large	large	ADJ
fcis-19688	23	22	-	-	PUNCT
fcis-19688	23	23	scale	scale	NOUN
fcis-19688	23	24	data	datum	NOUN
fcis-19688	23	25	,	,	PUNCT
fcis-19688	23	26	and	and	CCONJ
fcis-19688	23	27	no	no	ADV
fcis-19688	23	28	longer	long	ADV
fcis-19688	23	29	depends	depend	VERB
fcis-19688	23	30	on	on	ADP
fcis-19688	23	31	manually	manually	ADV
fcis-19688	23	32	designed	design	VERB
fcis-19688	23	33	features	feature	NOUN
fcis-19688	23	34	,	,	PUNCT
fcis-19688	23	35	thus	thus	ADV
fcis-19688	23	36	achieving	achieve	VERB
fcis-19688	23	37	great	great	ADJ
fcis-19688	23	38	success	success	NOUN
fcis-19688	23	39	in	in	ADP
fcis-19688	23	40	medical	medical	ADJ
fcis-19688	23	41	image	image	NOUN
fcis-19688	23	42	processing	processing	NOUN
fcis-19688	23	43	tasks	task	NOUN
fcis-19688	23	44	.	.	PUNCT
fcis-19688	24	1	in	in	ADP
fcis-19688	24	2	medical	medical	ADJ
fcis-19688	24	3	image	image	NOUN
fcis-19688	24	4	processing	processing	NOUN
fcis-19688	24	5	,	,	PUNCT
fcis-19688	24	6	deep	deep	ADJ
fcis-19688	24	7	learning	learning	NOUN
fcis-19688	24	8	algorithm	algorithm	NOUN
fcis-19688	24	9	has	have	AUX
fcis-19688	24	10	been	be	AUX
fcis-19688	24	11	widely	widely	ADV
fcis-19688	24	12	used	use	VERB
fcis-19688	24	13	in	in	ADP
fcis-19688	24	14	various	various	ADJ
fcis-19688	24	15	tasks	task	NOUN
fcis-19688	24	16	.	.	PUNCT
fcis-19688	25	1	through	through	ADP
fcis-19688	25	2	deep	deep	ADJ
fcis-19688	25	3	learning	learning	NOUN
fcis-19688	25	4	model	model	NOUN
fcis-19688	25	5	,	,	PUNCT
fcis-19688	25	6	different	different	ADJ
fcis-19688	25	7	tissues	tissue	NOUN
fcis-19688	25	8	or	or	CCONJ
fcis-19688	25	9	organs	organ	NOUN
fcis-19688	25	10	in	in	ADP
fcis-19688	25	11	medical	medical	ADJ
fcis-19688	25	12	images	image	NOUN
fcis-19688	25	13	can	can	AUX
fcis-19688	25	14	be	be	AUX
fcis-19688	25	15	accurately	accurately	ADV
fcis-19688	25	16	segmented	segment	VERB
fcis-19688	25	17	,	,	PUNCT
fcis-19688	25	18	such	such	ADJ
fcis-19688	25	19	as	as	ADP
fcis-19688	25	20	tumor	tumor	NOUN
fcis-19688	25	21	segmentation	segmentation	NOUN
fcis-19688	25	22	and	and	CCONJ
fcis-19688	25	23	brain	brain	NOUN
fcis-19688	25	24	segmentation	segmentation	NOUN
fcis-19688	25	25	[	[	X
fcis-19688	25	26	6	6	NUM
fcis-19688	25	27	-	-	SYM
fcis-19688	25	28	7	7	NUM
fcis-19688	25	29	]	]	PUNCT
fcis-19688	25	30	.	.	PUNCT
fcis-19688	26	1	the	the	DET
fcis-19688	26	2	deep	deep	ADJ
fcis-19688	26	3	learning	learning	NOUN
fcis-19688	26	4	algorithm	algorithm	NOUN
fcis-19688	26	5	is	be	AUX
fcis-19688	26	6	used	use	VERB
fcis-19688	26	7	to	to	PART
fcis-19688	26	8	detect	detect	VERB
fcis-19688	26	9	and	and	CCONJ
fcis-19688	26	10	diagnose	diagnose	VERB
fcis-19688	26	11	abnormal	abnormal	ADJ
fcis-19688	26	12	structures	structure	NOUN
fcis-19688	26	13	or	or	CCONJ
fcis-19688	26	14	features	feature	NOUN
fcis-19688	26	15	in	in	ADP
fcis-19688	26	16	medical	medical	ADJ
fcis-19688	26	17	images	image	NOUN
fcis-19688	26	18	,	,	PUNCT
fcis-19688	26	19	such	such	ADJ
fcis-19688	26	20	as	as	ADP
fcis-19688	26	21	lung	lung	NOUN
fcis-19688	26	22	nodule	nodule	NOUN
fcis-19688	26	23	detection	detection	NOUN
fcis-19688	26	24	and	and	CCONJ
fcis-19688	26	25	diabetic	diabetic	ADJ
fcis-19688	26	26	retinopathy	retinopathy	ADJ
fcis-19688	26	27	recognition	recognition	NOUN
fcis-19688	26	28	.	.	PUNCT
fcis-19688	27	1	the	the	DET
fcis-19688	27	2	deep	deep	ADJ
fcis-19688	27	3	learning	learning	NOUN
fcis-19688	27	4	algorithm	algorithm	NOUN
fcis-19688	27	5	is	be	AUX
fcis-19688	27	6	used	use	VERB
fcis-19688	27	7	to	to	PART
fcis-19688	27	8	denoise	denoise	VERB
fcis-19688	27	9	and	and	CCONJ
fcis-19688	27	10	reconstruct	reconstruct	VERB
fcis-19688	27	11	medical	medical	ADJ
fcis-19688	27	12	images	image	NOUN
fcis-19688	27	13	with	with	ADP
fcis-19688	27	14	super	super	ADJ
fcis-19688	27	15	resolution	resolution	NOUN
fcis-19688	27	16	,	,	PUNCT
fcis-19688	27	17	so	so	SCONJ
fcis-19688	27	18	as	as	SCONJ
fcis-19688	27	19	to	to	PART
fcis-19688	27	20	improve	improve	VERB
fcis-19688	27	21	the	the	DET
fcis-19688	27	22	image	image	NOUN
fcis-19688	27	23	quality	quality	NOUN
fcis-19688	27	24	and	and	CCONJ
fcis-19688	27	25	resolution	resolution	NOUN
fcis-19688	27	26	.	.	PUNCT
fcis-19688	28	1	although	although	SCONJ
fcis-19688	28	2	deep	deep	ADJ
fcis-19688	28	3	learning	learning	NOUN
fcis-19688	28	4	has	have	AUX
fcis-19688	28	5	achieved	achieve	VERB
fcis-19688	28	6	great	great	ADJ
fcis-19688	28	7	success	success	NOUN
fcis-19688	28	8	in	in	ADP
fcis-19688	28	9	medical	medical	ADJ
fcis-19688	28	10	image	image	NOUN
fcis-19688	28	11	processing	processing	NOUN
fcis-19688	28	12	,	,	PUNCT
fcis-19688	28	13	its	its	PRON
fcis-19688	28	14	application	application	NOUN
fcis-19688	28	15	still	still	ADV
fcis-19688	28	16	faces	face	VERB
fcis-19688	28	17	some	some	DET
fcis-19688	28	18	challenges	challenge	NOUN
fcis-19688	28	19	.	.	PUNCT
fcis-19688	29	1	first	first	ADV
fcis-19688	29	2	of	of	ADP
fcis-19688	29	3	all	all	PRON
fcis-19688	29	4	,	,	PUNCT
fcis-19688	29	5	medical	medical	ADJ
fcis-19688	29	6	image	image	NOUN
fcis-19688	29	7	data	datum	NOUN
fcis-19688	29	8	are	be	AUX
fcis-19688	29	9	often	often	ADV
fcis-19688	29	10	scarce	scarce	ADJ
fcis-19688	29	11	and	and	CCONJ
fcis-19688	29	12	the	the	DET
fcis-19688	29	13	labeling	labeling	NOUN
fcis-19688	29	14	cost	cost	NOUN
fcis-19688	29	15	is	be	AUX
fcis-19688	29	16	high	high	ADJ
fcis-19688	29	17	,	,	PUNCT
fcis-19688	29	18	so	so	ADV
fcis-19688	29	19	how	how	SCONJ
fcis-19688	29	20	to	to	PART
fcis-19688	29	21	effectively	effectively	ADV
fcis-19688	29	22	use	use	VERB
fcis-19688	29	23	limited	limited	ADJ
fcis-19688	29	24	data	data	NOUN
fcis-19688	29	25	resources	resource	NOUN
fcis-19688	29	26	for	for	ADP
fcis-19688	29	27	model	model	NOUN
fcis-19688	29	28	training	training	NOUN
fcis-19688	29	29	has	have	AUX
fcis-19688	29	30	become	become	VERB
fcis-19688	29	31	an	an	DET
fcis-19688	29	32	important	important	ADJ
fcis-19688	29	33	issue	issue	NOUN
fcis-19688	29	34	.	.	PUNCT
fcis-19688	30	1	secondly	secondly	ADV
fcis-19688	30	2	,	,	PUNCT
fcis-19688	30	3	the	the	DET
fcis-19688	30	4	interpretability	interpretability	NOUN
fcis-19688	30	5	and	and	CCONJ
fcis-19688	30	6	stability	stability	NOUN
fcis-19688	30	7	of	of	ADP
fcis-19688	30	8	deep	deep	ADJ
fcis-19688	30	9	learning	learning	NOUN
fcis-19688	30	10	model	model	NOUN
fcis-19688	30	11	still	still	ADV
fcis-19688	30	12	have	have	VERB
fcis-19688	30	13	some	some	DET
fcis-19688	30	14	limitations	limitation	NOUN
fcis-19688	30	15	,	,	PUNCT
fcis-19688	30	16	which	which	PRON
fcis-19688	30	17	limit	limit	VERB
fcis-19688	30	18	its	its	PRON
fcis-19688	30	19	application	application	NOUN
fcis-19688	30	20	in	in	ADP
fcis-19688	30	21	clinical	clinical	ADJ
fcis-19688	30	22	practice	practice	NOUN
fcis-19688	30	23	.	.	PUNCT
fcis-19688	31	1	in	in	ADP
fcis-19688	31	2	addition	addition	NOUN
fcis-19688	31	3	,	,	PUNCT
fcis-19688	31	4	the	the	DET
fcis-19688	31	5	diversity	diversity	NOUN
fcis-19688	31	6	and	and	CCONJ
fcis-19688	31	7	68	68	NUM
fcis-19688	31	8	complexity	complexity	NOUN
fcis-19688	31	9	of	of	ADP
fcis-19688	31	10	medical	medical	ADJ
fcis-19688	31	11	image	image	NOUN
fcis-19688	31	12	data	datum	NOUN
fcis-19688	31	13	also	also	ADV
fcis-19688	31	14	bring	bring	VERB
fcis-19688	31	15	challenges	challenge	NOUN
fcis-19688	31	16	to	to	ADP
fcis-19688	31	17	the	the	DET
fcis-19688	31	18	design	design	NOUN
fcis-19688	31	19	and	and	CCONJ
fcis-19688	31	20	optimization	optimization	NOUN
fcis-19688	31	21	of	of	ADP
fcis-19688	31	22	deep	deep	ADJ
fcis-19688	31	23	learning	learning	NOUN
fcis-19688	31	24	models	model	NOUN
fcis-19688	31	25	.	.	PUNCT
fcis-19688	32	1	3	3	X
fcis-19688	32	2	.	.	X
fcis-19688	32	3	optimization	optimization	NOUN
fcis-19688	32	4	method	method	NOUN
fcis-19688	32	5	of	of	ADP
fcis-19688	32	6	deep	deep	ADJ
fcis-19688	32	7	learning	learning	NOUN
fcis-19688	32	8	algorithm	algorithm	NOUN
fcis-19688	32	9	in	in	ADP
fcis-19688	32	10	deep	deep	ADJ
fcis-19688	32	11	learning	learning	NOUN
fcis-19688	32	12	,	,	PUNCT
fcis-19688	32	13	the	the	DET
fcis-19688	32	14	choice	choice	NOUN
fcis-19688	32	15	of	of	ADP
fcis-19688	32	16	learning	learn	VERB
fcis-19688	32	17	rate	rate	NOUN
fcis-19688	32	18	is	be	AUX
fcis-19688	32	19	very	very	ADV
fcis-19688	32	20	important	important	ADJ
fcis-19688	32	21	to	to	ADP
fcis-19688	32	22	the	the	DET
fcis-19688	32	23	performance	performance	NOUN
fcis-19688	32	24	of	of	ADP
fcis-19688	32	25	the	the	DET
fcis-19688	32	26	model	model	NOUN
fcis-19688	32	27	.	.	PUNCT
fcis-19688	33	1	traditional	traditional	ADJ
fcis-19688	33	2	learning	learning	NOUN
fcis-19688	33	3	rate	rate	NOUN
fcis-19688	33	4	scheduling	scheduling	NOUN
fcis-19688	33	5	methods	method	NOUN
fcis-19688	33	6	,	,	PUNCT
fcis-19688	33	7	such	such	ADJ
fcis-19688	33	8	as	as	ADP
fcis-19688	33	9	fixing	fix	VERB
fcis-19688	33	10	the	the	DET
fcis-19688	33	11	learning	learning	NOUN
fcis-19688	33	12	rate	rate	NOUN
fcis-19688	33	13	or	or	CCONJ
fcis-19688	33	14	adjusting	adjust	VERB
fcis-19688	33	15	the	the	DET
fcis-19688	33	16	learning	learning	NOUN
fcis-19688	33	17	rate	rate	NOUN
fcis-19688	33	18	according	accord	VERB
fcis-19688	33	19	to	to	ADP
fcis-19688	33	20	a	a	DET
fcis-19688	33	21	predetermined	predetermine	VERB
fcis-19688	33	22	schedule	schedule	NOUN
fcis-19688	33	23	,	,	PUNCT
fcis-19688	33	24	may	may	AUX
fcis-19688	33	25	not	not	PART
fcis-19688	33	26	fully	fully	ADV
fcis-19688	33	27	adapt	adapt	VERB
fcis-19688	33	28	to	to	ADP
fcis-19688	33	29	the	the	DET
fcis-19688	33	30	changes	change	NOUN
fcis-19688	33	31	of	of	ADP
fcis-19688	33	32	different	different	ADJ
fcis-19688	33	33	parameters	parameter	NOUN
fcis-19688	33	34	during	during	ADP
fcis-19688	33	35	model	model	NOUN
fcis-19688	33	36	training	training	NOUN
fcis-19688	33	37	[	[	X
fcis-19688	33	38	8	8	NUM
fcis-19688	33	39	-	-	SYM
fcis-19688	33	40	9	9	NUM
fcis-19688	33	41	]	]	PUNCT
fcis-19688	33	42	.	.	PUNCT
fcis-19688	34	1	therefore	therefore	ADV
fcis-19688	34	2	,	,	PUNCT
fcis-19688	34	3	it	it	PRON
fcis-19688	34	4	is	be	AUX
fcis-19688	34	5	necessary	necessary	ADJ
fcis-19688	34	6	to	to	PART
fcis-19688	34	7	design	design	VERB
fcis-19688	34	8	a	a	DET
fcis-19688	34	9	parameter	parameter	NOUN
fcis-19688	34	10	updating	update	VERB
fcis-19688	34	11	strategy	strategy	NOUN
fcis-19688	34	12	of	of	ADP
fcis-19688	34	13	deep	deep	ADJ
fcis-19688	34	14	learning	learning	NOUN
fcis-19688	34	15	model	model	NOUN
fcis-19688	34	16	with	with	ADP
fcis-19688	34	17	adaptive	adaptive	ADJ
fcis-19688	34	18	learning	learning	NOUN
fcis-19688	34	19	rate	rate	NOUN
fcis-19688	34	20	.	.	PUNCT
fcis-19688	35	1	the	the	DET
fcis-19688	35	2	method	method	NOUN
fcis-19688	35	3	proposed	propose	VERB
fcis-19688	35	4	in	in	ADP
fcis-19688	35	5	this	this	DET
fcis-19688	35	6	paper	paper	NOUN
fcis-19688	35	7	is	be	AUX
fcis-19688	35	8	a	a	DET
fcis-19688	35	9	dynamic	dynamic	ADJ
fcis-19688	35	10	adjustment	adjustment	NOUN
fcis-19688	35	11	strategy	strategy	NOUN
fcis-19688	35	12	based	base	VERB
fcis-19688	35	13	on	on	ADP
fcis-19688	35	14	adaptive	adaptive	ADJ
fcis-19688	35	15	learning	learning	NOUN
fcis-19688	35	16	rate	rate	NOUN
fcis-19688	35	17	,	,	PUNCT
fcis-19688	35	18	which	which	PRON
fcis-19688	35	19	can	can	AUX
fcis-19688	35	20	automatically	automatically	ADV
fcis-19688	35	21	adjust	adjust	VERB
fcis-19688	35	22	the	the	DET
fcis-19688	35	23	learning	learning	NOUN
fcis-19688	35	24	rate	rate	NOUN
fcis-19688	35	25	according	accord	VERB
fcis-19688	35	26	to	to	ADP
fcis-19688	35	27	the	the	DET
fcis-19688	35	28	historical	historical	ADJ
fcis-19688	35	29	update	update	NOUN
fcis-19688	35	30	of	of	ADP
fcis-19688	35	31	parameters	parameter	NOUN
fcis-19688	35	32	to	to	PART
fcis-19688	35	33	achieve	achieve	VERB
fcis-19688	35	34	more	more	ADV
fcis-19688	35	35	efficient	efficient	ADJ
fcis-19688	35	36	parameter	parameter	NOUN
fcis-19688	35	37	update	update	NOUN
fcis-19688	35	38	[	[	X
fcis-19688	35	39	10	10	NUM
fcis-19688	35	40	]	]	PUNCT
fcis-19688	35	41	.	.	PUNCT
fcis-19688	36	1	specifically	specifically	ADV
fcis-19688	36	2	,	,	PUNCT
fcis-19688	36	3	we	we	PRON
fcis-19688	36	4	adopt	adopt	VERB
fcis-19688	36	5	adaptive	adaptive	ADJ
fcis-19688	36	6	momentum	momentum	NOUN
fcis-19688	36	7	algorithm	algorithm	NOUN
fcis-19688	36	8	combined	combine	VERB
fcis-19688	36	9	with	with	ADP
fcis-19688	36	10	dynamic	dynamic	ADJ
fcis-19688	36	11	learning	learning	NOUN
fcis-19688	36	12	rate	rate	NOUN
fcis-19688	36	13	adjustment	adjustment	NOUN
fcis-19688	36	14	strategy	strategy	NOUN
fcis-19688	36	15	to	to	PART
fcis-19688	36	16	balance	balance	VERB
fcis-19688	36	17	convergence	convergence	NOUN
fcis-19688	36	18	speed	speed	NOUN
fcis-19688	36	19	and	and	CCONJ
fcis-19688	36	20	convergence	convergence	NOUN
fcis-19688	36	21	stability	stability	NOUN
fcis-19688	36	22	in	in	ADP
fcis-19688	36	23	the	the	DET
fcis-19688	36	24	training	training	NOUN
fcis-19688	36	25	process	process	NOUN
fcis-19688	36	26	.	.	PUNCT
fcis-19688	37	1	the	the	DET
fcis-19688	37	2	algorithm	algorithm	NOUN
fcis-19688	37	3	is	be	AUX
fcis-19688	37	4	described	describe	VERB
fcis-19688	37	5	as	as	ADP
fcis-19688	37	6	follows	follow	VERB
fcis-19688	37	7	:	:	PUNCT
fcis-19688	37	8	set	set	VERB
fcis-19688	37	9	the	the	DET
fcis-19688	37	10	initial	initial	ADJ
fcis-19688	37	11	learning	learning	NOUN
fcis-19688	37	12	rate	rate	NOUN
fcis-19688	37	13	as	as	ADP
fcis-19688	37	14	0	0	NUM
fcis-19688	37	15	and	and	CCONJ
fcis-19688	37	16	the	the	DET
fcis-19688	37	17	initial	initial	ADJ
fcis-19688	37	18	momentum	momentum	NOUN
fcis-19688	37	19	parameter	parameter	NOUN
fcis-19688	37	20	as	as	ADP
fcis-19688	37	21	0	0	NOUN
fcis-19688	37	22	.	.	PUNCT
fcis-19688	38	1	at	at	ADP
fcis-19688	38	2	each	each	DET
fcis-19688	38	3	parameter	parameter	NOUN
fcis-19688	38	4	update	update	NOUN
fcis-19688	38	5	,	,	PUNCT
fcis-19688	38	6	the	the	DET
fcis-19688	38	7	gradient	gradient	NOUN
fcis-19688	38	8	tg	tg	PROPN
fcis-19688	38	9	of	of	ADP
fcis-19688	38	10	the	the	DET
fcis-19688	38	11	current	current	ADJ
fcis-19688	38	12	parameter	parameter	NOUN
fcis-19688	38	13	is	be	AUX
fcis-19688	38	14	calculated	calculate	VERB
fcis-19688	38	15	.	.	PUNCT
fcis-19688	39	1	updating	update	VERB
fcis-19688	39	2	momentum	momentum	NOUN
fcis-19688	39	3	parameters	parameter	NOUN
fcis-19688	39	4	according	accord	VERB
fcis-19688	39	5	to	to	ADP
fcis-19688	39	6	historical	historical	ADJ
fcis-19688	39	7	gradient	gradient	NOUN
fcis-19688	39	8	information	information	NOUN
fcis-19688	39	9	:	:	PUNCT
fcis-19688	39	10			NOUN
fcis-19688	39	11	11	11	PUNCT
fcis-19688	39	12			NUM
fcis-19688	39	13			PROPN
fcis-19688	39	14	ttt	ttt	PROPN
fcis-19688	39	15	gsignαββ	gsignαββ	NOUN
fcis-19688	39	16	(	(	PUNCT
fcis-19688	39	17	1	1	NUM
fcis-19688	39	18	)	)	PUNCT
fcis-19688	39	19	where	where	SCONJ
fcis-19688	39	20			NOUN
fcis-19688	39	21	is	be	AUX
fcis-19688	39	22	the	the	DET
fcis-19688	39	23	momentum	momentum	NOUN
fcis-19688	39	24	adjustment	adjustment	NOUN
fcis-19688	39	25	coefficient	coefficient	NOUN
fcis-19688	39	26	and	and	CCONJ
fcis-19688	39	27			PROPN
fcis-19688	39	28	1tgsign	1tgsign	NOUN
fcis-19688	39	29	represents	represent	VERB
fcis-19688	39	30	the	the	DET
fcis-19688	39	31	sign	sign	NOUN
fcis-19688	39	32	of	of	ADP
fcis-19688	39	33	gradient	gradient	NOUN
fcis-19688	39	34	.	.	PUNCT
fcis-19688	40	1	update	update	VERB
fcis-19688	40	2	the	the	DET
fcis-19688	40	3	learning	learning	NOUN
fcis-19688	40	4	rate	rate	NOUN
fcis-19688	40	5	according	accord	VERB
fcis-19688	40	6	to	to	ADP
fcis-19688	40	7	the	the	DET
fcis-19688	40	8	momentum	momentum	NOUN
fcis-19688	40	9	parameters	parameter	NOUN
fcis-19688	40	10	:	:	PUNCT
fcis-19688	40	11			NOUN
fcis-19688	40	12	tt	tt	NOUN
fcis-19688	41	1			NUM
fcis-19688	41	2			NOUN
fcis-19688	41	3	exp0	exp0	PROPN
fcis-19688	41	4	(	(	PUNCT
fcis-19688	41	5	2	2	X
fcis-19688	41	6	)	)	PUNCT
fcis-19688	41	7	where	where	SCONJ
fcis-19688	41	8			PROPN
fcis-19688	41	9	is	be	AUX
fcis-19688	41	10	the	the	DET
fcis-19688	41	11	learning	learning	NOUN
fcis-19688	41	12	rate	rate	NOUN
fcis-19688	41	13	adjustment	adjustment	NOUN
fcis-19688	41	14	coefficient	coefficient	NOUN
fcis-19688	41	15	.	.	PUNCT
fcis-19688	42	1	updating	update	VERB
fcis-19688	42	2	the	the	DET
fcis-19688	42	3	parameters	parameter	NOUN
fcis-19688	42	4	by	by	ADP
fcis-19688	42	5	using	use	VERB
fcis-19688	42	6	the	the	DET
fcis-19688	42	7	updated	update	VERB
fcis-19688	42	8	learning	learning	NOUN
fcis-19688	42	9	rate	rate	NOUN
fcis-19688	42	10	and	and	CCONJ
fcis-19688	42	11	momentum	momentum	NOUN
fcis-19688	42	12	parameters	parameter	NOUN
fcis-19688	42	13	;	;	PUNCT
fcis-19688	42	14	tttt	tttt	X
fcis-19688	42	15	g	g	PROPN
fcis-19688	42	16			NOUN
fcis-19688	42	17	1	1	NUM
fcis-19688	42	18	(	(	PUNCT
fcis-19688	42	19	3	3	NUM
fcis-19688	42	20	)	)	PUNCT
fcis-19688	42	21	repeat	repeat	VERB
fcis-19688	42	22	the	the	DET
fcis-19688	42	23	above	above	ADJ
fcis-19688	42	24	steps	step	NOUN
fcis-19688	42	25	until	until	SCONJ
fcis-19688	42	26	the	the	DET
fcis-19688	42	27	stop	stop	NOUN
fcis-19688	42	28	condition	condition	NOUN
fcis-19688	42	29	is	be	AUX
fcis-19688	42	30	reached	reach	VERB
fcis-19688	42	31	.	.	PUNCT
fcis-19688	43	1	the	the	DET
fcis-19688	43	2	innovation	innovation	NOUN
fcis-19688	43	3	of	of	ADP
fcis-19688	43	4	this	this	DET
fcis-19688	43	5	method	method	NOUN
fcis-19688	43	6	lies	lie	VERB
fcis-19688	43	7	in	in	ADP
fcis-19688	43	8	the	the	DET
fcis-19688	43	9	joint	joint	ADJ
fcis-19688	43	10	optimization	optimization	NOUN
fcis-19688	43	11	of	of	ADP
fcis-19688	43	12	momentum	momentum	NOUN
fcis-19688	43	13	parameters	parameter	NOUN
fcis-19688	43	14	and	and	CCONJ
fcis-19688	43	15	learning	learn	VERB
fcis-19688	43	16	rate	rate	NOUN
fcis-19688	43	17	,	,	PUNCT
fcis-19688	43	18	and	and	CCONJ
fcis-19688	43	19	through	through	ADP
fcis-19688	43	20	adaptive	adaptive	ADJ
fcis-19688	43	21	adjustment	adjustment	NOUN
fcis-19688	43	22	of	of	ADP
fcis-19688	43	23	learning	learn	VERB
fcis-19688	43	24	rate	rate	NOUN
fcis-19688	43	25	,	,	PUNCT
fcis-19688	43	26	it	it	PRON
fcis-19688	43	27	can	can	AUX
fcis-19688	43	28	better	well	ADV
fcis-19688	43	29	balance	balance	VERB
fcis-19688	43	30	convergence	convergence	NOUN
fcis-19688	43	31	speed	speed	NOUN
fcis-19688	43	32	and	and	CCONJ
fcis-19688	43	33	convergence	convergence	NOUN
fcis-19688	43	34	stability	stability	NOUN
fcis-19688	43	35	in	in	ADP
fcis-19688	43	36	the	the	DET
fcis-19688	43	37	training	training	NOUN
fcis-19688	43	38	process	process	NOUN
fcis-19688	43	39	.	.	PUNCT
fcis-19688	44	1	compared	compare	VERB
fcis-19688	44	2	with	with	ADP
fcis-19688	44	3	the	the	DET
fcis-19688	44	4	traditional	traditional	ADJ
fcis-19688	44	5	fixed	fix	VERB
fcis-19688	44	6	learning	learning	NOUN
fcis-19688	44	7	rate	rate	NOUN
fcis-19688	44	8	method	method	NOUN
fcis-19688	44	9	,	,	PUNCT
fcis-19688	44	10	this	this	DET
fcis-19688	44	11	method	method	NOUN
fcis-19688	44	12	can	can	AUX
fcis-19688	44	13	better	well	ADV
fcis-19688	44	14	adapt	adapt	VERB
fcis-19688	44	15	to	to	ADP
fcis-19688	44	16	the	the	DET
fcis-19688	44	17	update	update	NOUN
fcis-19688	44	18	of	of	ADP
fcis-19688	44	19	different	different	ADJ
fcis-19688	44	20	parameters	parameter	NOUN
fcis-19688	44	21	,	,	PUNCT
fcis-19688	44	22	thus	thus	ADV
fcis-19688	44	23	improving	improve	VERB
fcis-19688	44	24	the	the	DET
fcis-19688	44	25	training	training	NOUN
fcis-19688	44	26	efficiency	efficiency	NOUN
fcis-19688	44	27	and	and	CCONJ
fcis-19688	44	28	performance	performance	NOUN
fcis-19688	44	29	of	of	ADP
fcis-19688	44	30	the	the	DET
fcis-19688	44	31	model	model	NOUN
fcis-19688	44	32	.	.	PUNCT
fcis-19688	45	1	4	4	X
fcis-19688	45	2	.	.	X
fcis-19688	45	3	experimental	experimental	ADJ
fcis-19688	45	4	design	design	NOUN
fcis-19688	45	5	and	and	CCONJ
fcis-19688	45	6	result	result	VERB
fcis-19688	45	7	analysis	analysis	NOUN
fcis-19688	45	8	in	in	ADP
fcis-19688	45	9	the	the	DET
fcis-19688	45	10	experiment	experiment	NOUN
fcis-19688	45	11	,	,	PUNCT
fcis-19688	45	12	the	the	DET
fcis-19688	45	13	public	public	ADJ
fcis-19688	45	14	medical	medical	ADJ
fcis-19688	45	15	image	image	NOUN
fcis-19688	45	16	data	data	NOUN
fcis-19688	45	17	sets	set	NOUN
fcis-19688	45	18	,	,	PUNCT
fcis-19688	45	19	brats	brat	NOUN
fcis-19688	45	20	(	(	PUNCT
fcis-19688	45	21	brain	brain	NOUN
fcis-19688	45	22	tumor	tumor	NOUN
fcis-19688	45	23	segmentation	segmentation	NOUN
fcis-19688	45	24	data	datum	NOUN
fcis-19688	45	25	set	set	VERB
fcis-19688	45	26	)	)	PUNCT
fcis-19688	45	27	and	and	CCONJ
fcis-19688	45	28	lidc	lidc	ADJ
fcis-19688	45	29	-	-	PUNCT
fcis-19688	45	30	idri	idri	NOUN
fcis-19688	45	31	(	(	PUNCT
fcis-19688	45	32	lung	lung	NOUN
fcis-19688	45	33	nodule	nodule	NOUN
fcis-19688	45	34	detection	detection	NOUN
fcis-19688	45	35	data	datum	NOUN
fcis-19688	45	36	set	set	NOUN
fcis-19688	45	37	)	)	PUNCT
fcis-19688	45	38	,	,	PUNCT
fcis-19688	45	39	which	which	PRON
fcis-19688	45	40	contain	contain	VERB
fcis-19688	45	41	different	different	ADJ
fcis-19688	45	42	types	type	NOUN
fcis-19688	45	43	of	of	ADP
fcis-19688	45	44	medical	medical	ADJ
fcis-19688	45	45	images	image	NOUN
fcis-19688	45	46	,	,	PUNCT
fcis-19688	45	47	are	be	AUX
fcis-19688	45	48	suitable	suitable	ADJ
fcis-19688	45	49	for	for	ADP
fcis-19688	45	50	the	the	DET
fcis-19688	45	51	training	training	NOUN
fcis-19688	45	52	and	and	CCONJ
fcis-19688	45	53	evaluation	evaluation	NOUN
fcis-19688	45	54	of	of	ADP
fcis-19688	45	55	deep	deep	ADJ
fcis-19688	45	56	learning	learning	NOUN
fcis-19688	45	57	models	model	NOUN
fcis-19688	45	58	[	[	X
fcis-19688	45	59	10	10	NUM
fcis-19688	45	60	-	-	SYM
fcis-19688	45	61	11	11	NUM
fcis-19688	45	62	]	]	PUNCT
fcis-19688	45	63	.	.	PUNCT
fcis-19688	46	1	experimental	experimental	ADJ
fcis-19688	46	2	environment	environment	NOUN
fcis-19688	46	3	configuration	configuration	NOUN
fcis-19688	46	4	:	:	PUNCT
fcis-19688	46	5	operating	operate	VERB
fcis-19688	46	6	system	system	NOUN
fcis-19688	46	7	:	:	PUNCT
fcis-19688	46	8	ubuntu	ubuntu	NOUN
fcis-19688	46	9	20.04	20.04	NUM
fcis-19688	46	10	lts	lts	NOUN
fcis-19688	46	11	;	;	PUNCT
fcis-19688	46	12	gpu	gpu	PROPN
fcis-19688	46	13	:	:	PUNCT
fcis-19688	46	14	nvidia	nvidia	PROPN
fcis-19688	46	15	geforce	geforce	NOUN
fcis-19688	46	16	rtx	rtx	PROPN
fcis-19688	46	17	3090	3090	NUM
fcis-19688	46	18	;	;	PUNCT
fcis-19688	46	19	cuda	cuda	NOUN
fcis-19688	46	20	version	version	NOUN
fcis-19688	46	21	:	:	PUNCT
fcis-19688	46	22	11.2	11.2	NUM
fcis-19688	46	23	;	;	PUNCT
fcis-19688	46	24	deep	deep	ADJ
fcis-19688	46	25	learning	learning	NOUN
fcis-19688	46	26	framework	framework	NOUN
fcis-19688	46	27	:	:	PUNCT
fcis-19688	46	28	pytorch	pytorch	NOUN
fcis-19688	46	29	1.9.0	1.9.0	NUM
fcis-19688	46	30	.	.	PUNCT
fcis-19688	47	1	firstly	firstly	ADV
fcis-19688	47	2	,	,	PUNCT
fcis-19688	47	3	the	the	DET
fcis-19688	47	4	medical	medical	ADJ
fcis-19688	47	5	image	image	NOUN
fcis-19688	47	6	is	be	AUX
fcis-19688	47	7	standardized	standardize	VERB
fcis-19688	47	8	and	and	CCONJ
fcis-19688	47	9	the	the	DET
fcis-19688	47	10	pixel	pixel	PROPN
fcis-19688	47	11	value	value	NOUN
fcis-19688	47	12	of	of	ADP
fcis-19688	47	13	the	the	DET
fcis-19688	47	14	image	image	NOUN
fcis-19688	47	15	is	be	AUX
fcis-19688	47	16	scaled	scale	VERB
fcis-19688	47	17	to	to	ADP
fcis-19688	47	18	a	a	DET
fcis-19688	47	19	fixed	fix	VERB
fcis-19688	47	20	range	range	NOUN
fcis-19688	47	21	,	,	PUNCT
fcis-19688	47	22	usually	usually	ADV
fcis-19688	47	23	[	[	X
fcis-19688	47	24	0	0	NUM
fcis-19688	47	25	,	,	PUNCT
fcis-19688	47	26	1	1	NUM
fcis-19688	47	27	]	]	PUNCT
fcis-19688	47	28	or	or	CCONJ
fcis-19688	47	29	[	[	X
fcis-19688	47	30	-1,1	-1,1	X
fcis-19688	47	31	]	]	X
fcis-19688	47	32	;	;	PUNCT
fcis-19688	47	33	secondly	secondly	ADV
fcis-19688	47	34	,	,	PUNCT
fcis-19688	47	35	the	the	DET
fcis-19688	47	36	image	image	NOUN
fcis-19688	47	37	is	be	AUX
fcis-19688	47	38	cropped	crop	VERB
fcis-19688	47	39	to	to	PART
fcis-19688	47	40	remove	remove	VERB
fcis-19688	47	41	useless	useless	ADJ
fcis-19688	47	42	edge	edge	NOUN
fcis-19688	47	43	information	information	NOUN
fcis-19688	47	44	;	;	PUNCT
fcis-19688	47	45	then	then	ADV
fcis-19688	47	46	,	,	PUNCT
fcis-19688	47	47	data	data	NOUN
fcis-19688	47	48	enhancement	enhancement	NOUN
fcis-19688	47	49	operations	operation	NOUN
fcis-19688	47	50	are	be	AUX
fcis-19688	47	51	carried	carry	VERB
fcis-19688	47	52	out	out	ADP
fcis-19688	47	53	,	,	PUNCT
fcis-19688	47	54	including	include	VERB
fcis-19688	47	55	random	random	ADJ
fcis-19688	47	56	rotation	rotation	NOUN
fcis-19688	47	57	,	,	PUNCT
fcis-19688	47	58	horizontal	horizontal	ADJ
fcis-19688	47	59	flipping	flipping	NOUN
fcis-19688	47	60	,	,	PUNCT
fcis-19688	47	61	vertical	vertical	ADJ
fcis-19688	47	62	flipping	flipping	NOUN
fcis-19688	47	63	,	,	PUNCT
fcis-19688	47	64	random	random	ADJ
fcis-19688	47	65	scaling	scaling	NOUN
fcis-19688	47	66	,	,	PUNCT
fcis-19688	47	67	etc	etc	X
fcis-19688	47	68	.	.	X
fcis-19688	47	69	,	,	PUNCT
fcis-19688	47	70	to	to	PART
fcis-19688	47	71	increase	increase	VERB
fcis-19688	47	72	the	the	DET
fcis-19688	47	73	diversity	diversity	NOUN
fcis-19688	47	74	of	of	ADP
fcis-19688	47	75	data	datum	NOUN
fcis-19688	47	76	and	and	CCONJ
fcis-19688	47	77	the	the	DET
fcis-19688	47	78	generalization	generalization	NOUN
fcis-19688	47	79	ability	ability	NOUN
fcis-19688	47	80	of	of	ADP
fcis-19688	47	81	the	the	DET
fcis-19688	47	82	model	model	NOUN
fcis-19688	47	83	;	;	PUNCT
fcis-19688	47	84	finally	finally	ADV
fcis-19688	47	85	,	,	PUNCT
fcis-19688	47	86	the	the	DET
fcis-19688	47	87	label	label	NOUN
fcis-19688	47	88	is	be	AUX
fcis-19688	47	89	smoothed	smooth	VERB
fcis-19688	47	90	,	,	PUNCT
fcis-19688	47	91	such	such	ADJ
fcis-19688	47	92	as	as	ADP
fcis-19688	47	93	gaussian	gaussian	ADJ
fcis-19688	47	94	filtering	filtering	NOUN
fcis-19688	47	95	or	or	CCONJ
fcis-19688	47	96	morphological	morphological	ADJ
fcis-19688	47	97	processing	processing	NOUN
fcis-19688	47	98	,	,	PUNCT
fcis-19688	47	99	to	to	PART
fcis-19688	47	100	make	make	VERB
fcis-19688	47	101	the	the	DET
fcis-19688	47	102	label	label	NOUN
fcis-19688	47	103	image	image	NOUN
fcis-19688	47	104	smoother	smooth	ADJ
fcis-19688	47	105	and	and	CCONJ
fcis-19688	47	106	more	more	ADV
fcis-19688	47	107	continuous	continuous	ADJ
fcis-19688	47	108	.	.	PUNCT
fcis-19688	48	1	select	select	ADJ
fcis-19688	48	2	deep	deep	ADJ
fcis-19688	48	3	learning	learning	NOUN
fcis-19688	48	4	models	model	NOUN
fcis-19688	48	5	(	(	PUNCT
fcis-19688	48	6	u	u	NOUN
fcis-19688	48	7	-	-	NOUN
fcis-19688	48	8	net	net	ADJ
fcis-19688	48	9	,	,	PUNCT
fcis-19688	48	10	deeplab	deeplab	NOUN
fcis-19688	48	11	,	,	PUNCT
fcis-19688	48	12	densenet	densenet	NOUN
fcis-19688	48	13	)	)	PUNCT
fcis-19688	48	14	suitable	suitable	ADJ
fcis-19688	48	15	for	for	ADP
fcis-19688	48	16	medical	medical	ADJ
fcis-19688	48	17	image	image	NOUN
fcis-19688	48	18	segmentation	segmentation	NOUN
fcis-19688	48	19	tasks[12	tasks[12	PROPN
fcis-19688	48	20	-	-	PUNCT
fcis-19688	48	21	14	14	NUM
fcis-19688	48	22	]	]	PUNCT
fcis-19688	48	23	.	.	PUNCT
fcis-19688	49	1	these	these	DET
fcis-19688	49	2	models	model	NOUN
fcis-19688	49	3	have	have	VERB
fcis-19688	49	4	good	good	ADJ
fcis-19688	49	5	feature	feature	NOUN
fcis-19688	49	6	extraction	extraction	NOUN
fcis-19688	49	7	and	and	CCONJ
fcis-19688	49	8	representation	representation	NOUN
fcis-19688	49	9	ability	ability	NOUN
fcis-19688	49	10	,	,	PUNCT
fcis-19688	49	11	and	and	CCONJ
fcis-19688	49	12	are	be	AUX
fcis-19688	49	13	suitable	suitable	ADJ
fcis-19688	49	14	for	for	ADP
fcis-19688	49	15	processing	processing	NOUN
fcis-19688	49	16	complex	complex	ADJ
fcis-19688	49	17	structures	structure	NOUN
fcis-19688	49	18	and	and	CCONJ
fcis-19688	49	19	information	information	NOUN
fcis-19688	49	20	in	in	ADP
fcis-19688	49	21	medical	medical	ADJ
fcis-19688	49	22	images	image	NOUN
fcis-19688	49	23	[	[	X
fcis-19688	49	24	15	15	NUM
fcis-19688	49	25	-	-	SYM
fcis-19688	49	26	16	16	NUM
fcis-19688	49	27	]	]	PUNCT
fcis-19688	49	28	.	.	PUNCT
fcis-19688	50	1	according	accord	VERB
fcis-19688	50	2	to	to	ADP
fcis-19688	50	3	the	the	DET
fcis-19688	50	4	task	task	NOUN
fcis-19688	50	5	requirements	requirement	NOUN
fcis-19688	50	6	and	and	CCONJ
fcis-19688	50	7	data	datum	NOUN
fcis-19688	50	8	characteristics	characteristic	NOUN
fcis-19688	50	9	,	,	PUNCT
fcis-19688	50	10	the	the	DET
fcis-19688	50	11	network	network	NOUN
fcis-19688	50	12	structure	structure	NOUN
fcis-19688	50	13	is	be	AUX
fcis-19688	50	14	designed	design	VERB
fcis-19688	50	15	,	,	PUNCT
fcis-19688	50	16	including	include	VERB
fcis-19688	50	17	convolution	convolution	NOUN
fcis-19688	50	18	layer	layer	NOUN
fcis-19688	50	19	,	,	PUNCT
fcis-19688	50	20	pooling	pool	VERB
fcis-19688	50	21	layer	layer	NOUN
fcis-19688	50	22	and	and	CCONJ
fcis-19688	50	23	upsampling	upsample	VERB
fcis-19688	50	24	layer	layer	NOUN
fcis-19688	50	25	,	,	PUNCT
fcis-19688	50	26	and	and	CCONJ
fcis-19688	50	27	regularization	regularization	NOUN
fcis-19688	50	28	layer	layer	NOUN
fcis-19688	50	29	and	and	CCONJ
fcis-19688	50	30	activation	activation	NOUN
fcis-19688	50	31	function	function	NOUN
fcis-19688	50	32	are	be	AUX
fcis-19688	50	33	added	add	VERB
fcis-19688	50	34	according	accord	VERB
fcis-19688	50	35	to	to	ADP
fcis-19688	50	36	the	the	DET
fcis-19688	50	37	actual	actual	ADJ
fcis-19688	50	38	situation[17	situation[17	NOUN
fcis-19688	50	39	-	-	PUNCT
fcis-19688	50	40	18	18	NUM
fcis-19688	50	41	]	]	PUNCT
fcis-19688	50	42	.	.	PUNCT
fcis-19688	51	1	cross	cross	PROPN
fcis-19688	51	2	entropy	entropy	PROPN
fcis-19688	51	3	loss	loss	NOUN
fcis-19688	51	4	is	be	AUX
fcis-19688	51	5	selected	select	VERB
fcis-19688	51	6	to	to	PART
fcis-19688	51	7	measure	measure	VERB
fcis-19688	51	8	the	the	DET
fcis-19688	51	9	difference	difference	NOUN
fcis-19688	51	10	between	between	ADP
fcis-19688	51	11	the	the	DET
fcis-19688	51	12	predicted	predict	VERB
fcis-19688	51	13	results	result	NOUN
fcis-19688	51	14	of	of	ADP
fcis-19688	51	15	the	the	DET
fcis-19688	51	16	model	model	NOUN
fcis-19688	51	17	and	and	CCONJ
fcis-19688	51	18	the	the	DET
fcis-19688	51	19	real	real	ADJ
fcis-19688	51	20	label[19	label[19	NOUN
fcis-19688	51	21	]	]	X
fcis-19688	51	22	.	.	PUNCT
fcis-19688	52	1	the	the	DET
fcis-19688	52	2	optimizer	optimizer	NOUN
fcis-19688	52	3	adam	adam	PROPN
fcis-19688	52	4	is	be	AUX
fcis-19688	52	5	used	use	VERB
fcis-19688	52	6	to	to	PART
fcis-19688	52	7	adjust	adjust	VERB
fcis-19688	52	8	the	the	DET
fcis-19688	52	9	model	model	NOUN
fcis-19688	52	10	parameters	parameter	NOUN
fcis-19688	52	11	to	to	PART
fcis-19688	52	12	minimize	minimize	VERB
fcis-19688	52	13	the	the	DET
fcis-19688	52	14	loss	loss	NOUN
fcis-19688	52	15	function[20	function[20	VERB
fcis-19688	52	16	-	-	SYM
fcis-19688	52	17	21	21	NUM
fcis-19688	52	18	]	]	PUNCT
fcis-19688	52	19	.	.	PUNCT
fcis-19688	53	1	the	the	DET
fcis-19688	53	2	selected	select	VERB
fcis-19688	53	3	medical	medical	ADJ
fcis-19688	53	4	image	image	NOUN
fcis-19688	53	5	data	datum	NOUN
fcis-19688	53	6	set	set	VERB
fcis-19688	53	7	is	be	AUX
fcis-19688	53	8	divided	divide	VERB
fcis-19688	53	9	into	into	ADP
fcis-19688	53	10	training	training	NOUN
fcis-19688	53	11	set	set	NOUN
fcis-19688	53	12	,	,	PUNCT
fcis-19688	53	13	verification	verification	NOUN
fcis-19688	53	14	set	set	NOUN
fcis-19688	53	15	and	and	CCONJ
fcis-19688	53	16	test	test	NOUN
fcis-19688	53	17	set	set	VERB
fcis-19688	53	18	.	.	PUNCT
fcis-19688	54	1	80	80	NUM
fcis-19688	54	2	%	%	NOUN
fcis-19688	54	3	data	datum	NOUN
fcis-19688	54	4	is	be	AUX
fcis-19688	54	5	used	use	VERB
fcis-19688	54	6	as	as	ADP
fcis-19688	54	7	training	training	NOUN
fcis-19688	54	8	set	set	NOUN
fcis-19688	54	9	,	,	PUNCT
fcis-19688	54	10	10	10	NUM
fcis-19688	54	11	%	%	NOUN
fcis-19688	54	12	data	datum	NOUN
fcis-19688	54	13	as	as	ADP
fcis-19688	54	14	verification	verification	NOUN
fcis-19688	54	15	set	set	VERB
fcis-19688	54	16	and	and	CCONJ
fcis-19688	54	17	10	10	NUM
fcis-19688	54	18	%	%	NOUN
fcis-19688	54	19	data	datum	NOUN
fcis-19688	54	20	as	as	ADP
fcis-19688	54	21	test	test	NOUN
fcis-19688	54	22	set	set	VERB
fcis-19688	54	23	.	.	PUNCT
fcis-19688	55	1	with	with	ADP
fcis-19688	55	2	the	the	DET
fcis-19688	55	3	progress	progress	NOUN
fcis-19688	55	4	of	of	ADP
fcis-19688	55	5	training	training	NOUN
fcis-19688	55	6	,	,	PUNCT
fcis-19688	55	7	the	the	DET
fcis-19688	55	8	loss	loss	NOUN
fcis-19688	55	9	of	of	ADP
fcis-19688	55	10	training	training	NOUN
fcis-19688	55	11	set	set	VERB
fcis-19688	55	12	gradually	gradually	ADV
fcis-19688	55	13	decreases	decrease	VERB
fcis-19688	55	14	,	,	PUNCT
fcis-19688	55	15	which	which	PRON
fcis-19688	55	16	shows	show	VERB
fcis-19688	55	17	that	that	SCONJ
fcis-19688	55	18	the	the	DET
fcis-19688	55	19	model	model	NOUN
fcis-19688	55	20	gradually	gradually	ADV
fcis-19688	55	21	learns	learn	VERB
fcis-19688	55	22	the	the	DET
fcis-19688	55	23	characteristics	characteristic	NOUN
fcis-19688	55	24	and	and	CCONJ
fcis-19688	55	25	patterns	pattern	NOUN
fcis-19688	55	26	of	of	ADP
fcis-19688	55	27	data	datum	NOUN
fcis-19688	55	28	during	during	ADP
fcis-19688	55	29	the	the	DET
fcis-19688	55	30	training	training	NOUN
fcis-19688	55	31	process	process	NOUN
fcis-19688	55	32	and	and	CCONJ
fcis-19688	55	33	obtains	obtain	VERB
fcis-19688	55	34	certain	certain	ADJ
fcis-19688	55	35	training	training	NOUN
fcis-19688	55	36	results	result	NOUN
fcis-19688	55	37	.	.	PUNCT
fcis-19688	56	1	at	at	ADP
fcis-19688	56	2	the	the	DET
fcis-19688	56	3	beginning	beginning	NOUN
fcis-19688	56	4	of	of	ADP
fcis-19688	56	5	training	training	NOUN
fcis-19688	56	6	,	,	PUNCT
fcis-19688	56	7	the	the	DET
fcis-19688	56	8	loss	loss	NOUN
fcis-19688	56	9	decreased	decrease	VERB
fcis-19688	56	10	rapidly	rapidly	ADV
fcis-19688	56	11	,	,	PUNCT
fcis-19688	56	12	but	but	CCONJ
fcis-19688	56	13	with	with	ADP
fcis-19688	56	14	the	the	DET
fcis-19688	56	15	progress	progress	NOUN
fcis-19688	56	16	of	of	ADP
fcis-19688	56	17	training	training	NOUN
fcis-19688	56	18	,	,	PUNCT
fcis-19688	56	19	the	the	DET
fcis-19688	56	20	decline	decline	NOUN
fcis-19688	56	21	rate	rate	NOUN
fcis-19688	56	22	gradually	gradually	ADV
fcis-19688	56	23	slowed	slow	VERB
fcis-19688	56	24	down	down	ADP
fcis-19688	56	25	and	and	CCONJ
fcis-19688	56	26	eventually	eventually	ADV
fcis-19688	56	27	stabilized	stabilize	VERB
fcis-19688	56	28	.	.	PUNCT
fcis-19688	57	1	compared	compare	VERB
fcis-19688	57	2	with	with	ADP
fcis-19688	57	3	the	the	DET
fcis-19688	57	4	training	training	NOUN
fcis-19688	57	5	set	set	NOUN
fcis-19688	57	6	,	,	PUNCT
fcis-19688	57	7	the	the	DET
fcis-19688	57	8	loss	loss	NOUN
fcis-19688	57	9	on	on	ADP
fcis-19688	57	10	the	the	DET
fcis-19688	57	11	verification	verification	NOUN
fcis-19688	57	12	set	set	NOUN
fcis-19688	57	13	usually	usually	ADV
fcis-19688	57	14	tends	tend	VERB
fcis-19688	57	15	to	to	PART
fcis-19688	57	16	decrease	decrease	VERB
fcis-19688	57	17	first	first	ADV
fcis-19688	57	18	and	and	CCONJ
fcis-19688	57	19	then	then	ADV
fcis-19688	57	20	increase	increase	VERB
fcis-19688	57	21	.	.	PUNCT
fcis-19688	58	1	at	at	ADP
fcis-19688	58	2	the	the	DET
fcis-19688	58	3	initial	initial	ADJ
fcis-19688	58	4	stage	stage	NOUN
fcis-19688	58	5	of	of	ADP
fcis-19688	58	6	training	training	NOUN
fcis-19688	58	7	,	,	PUNCT
fcis-19688	58	8	because	because	SCONJ
fcis-19688	58	9	the	the	DET
fcis-19688	58	10	model	model	NOUN
fcis-19688	58	11	over	over	ADV
fcis-19688	58	12	-	-	PUNCT
fcis-19688	58	13	fits	fit	VERB
fcis-19688	58	14	the	the	DET
fcis-19688	58	15	training	training	NOUN
fcis-19688	58	16	data	datum	NOUN
fcis-19688	58	17	,	,	PUNCT
fcis-19688	58	18	the	the	DET
fcis-19688	58	19	loss	loss	NOUN
fcis-19688	58	20	on	on	ADP
fcis-19688	58	21	the	the	DET
fcis-19688	58	22	verification	verification	NOUN
fcis-19688	58	23	set	set	NOUN
fcis-19688	58	24	will	will	AUX
fcis-19688	58	25	be	be	AUX
fcis-19688	58	26	greater	great	ADJ
fcis-19688	58	27	;	;	PUNCT
fcis-19688	58	28	with	with	ADP
fcis-19688	58	29	the	the	DET
fcis-19688	58	30	training	training	NOUN
fcis-19688	58	31	,	,	PUNCT
fcis-19688	58	32	the	the	DET
fcis-19688	58	33	model	model	NOUN
fcis-19688	58	34	gradually	gradually	ADV
fcis-19688	58	35	learns	learn	VERB
fcis-19688	58	36	the	the	DET
fcis-19688	58	37	general	general	ADJ
fcis-19688	58	38	characteristics	characteristic	NOUN
fcis-19688	58	39	of	of	ADP
fcis-19688	58	40	data	datum	NOUN
fcis-19688	58	41	,	,	PUNCT
fcis-19688	58	42	and	and	CCONJ
fcis-19688	58	43	the	the	DET
fcis-19688	58	44	loss	loss	NOUN
fcis-19688	58	45	on	on	ADP
fcis-19688	58	46	the	the	DET
fcis-19688	58	47	verification	verification	NOUN
fcis-19688	58	48	set	set	NOUN
fcis-19688	58	49	begins	begin	VERB
fcis-19688	58	50	to	to	PART
fcis-19688	58	51	decrease	decrease	VERB
fcis-19688	58	52	;	;	PUNCT
fcis-19688	58	53	however	however	ADV
fcis-19688	58	54	,	,	PUNCT
fcis-19688	58	55	when	when	SCONJ
fcis-19688	58	56	the	the	DET
fcis-19688	58	57	model	model	NOUN
fcis-19688	58	58	begins	begin	VERB
fcis-19688	58	59	to	to	PART
fcis-19688	58	60	appear	appear	VERB
fcis-19688	58	61	over	over	ADP
fcis-19688	58	62	-	-	PUNCT
fcis-19688	58	63	fitting	fitting	ADJ
fcis-19688	58	64	,	,	PUNCT
fcis-19688	58	65	the	the	DET
fcis-19688	58	66	loss	loss	NOUN
fcis-19688	58	67	on	on	ADP
fcis-19688	58	68	the	the	DET
fcis-19688	58	69	verification	verification	NOUN
fcis-19688	58	70	set	set	NOUN
fcis-19688	58	71	will	will	AUX
fcis-19688	58	72	increase	increase	VERB
fcis-19688	58	73	again	again	ADV
fcis-19688	58	74	,	,	PUNCT
fcis-19688	58	75	which	which	PRON
fcis-19688	58	76	indicates	indicate	VERB
fcis-19688	58	77	that	that	SCONJ
fcis-19688	58	78	the	the	DET
fcis-19688	58	79	model	model	NOUN
fcis-19688	58	80	has	have	AUX
fcis-19688	58	81	learned	learn	VERB
fcis-19688	58	82	the	the	DET
fcis-19688	58	83	training	training	NOUN
fcis-19688	58	84	data	datum	NOUN
fcis-19688	58	85	too	too	ADV
fcis-19688	58	86	deeply	deeply	ADV
fcis-19688	58	87	and	and	CCONJ
fcis-19688	58	88	lost	lose	VERB
fcis-19688	58	89	its	its	PRON
fcis-19688	58	90	generalization	generalization	NOUN
fcis-19688	58	91	ability	ability	NOUN
fcis-19688	58	92	.	.	PUNCT
fcis-19688	59	1	see	see	VERB
fcis-19688	59	2	figure	figure	NOUN
fcis-19688	59	3	1	1	NUM
fcis-19688	59	4	for	for	ADP
fcis-19688	59	5	details	detail	NOUN
fcis-19688	59	6	.	.	PUNCT
fcis-19688	60	1	figure	figure	NOUN
fcis-19688	60	2	2	2	NUM
fcis-19688	60	3	shows	show	VERB
fcis-19688	60	4	the	the	DET
fcis-19688	60	5	prediction	prediction	NOUN
fcis-19688	60	6	accuracy	accuracy	NOUN
fcis-19688	60	7	and	and	CCONJ
fcis-19688	60	8	errors	error	NOUN
fcis-19688	60	9	of	of	ADP
fcis-19688	60	10	u	u	NOUN
fcis-19688	60	11	-	-	NOUN
fcis-19688	60	12	net	net	ADJ
fcis-19688	60	13	,	,	PUNCT
fcis-19688	60	14	deeplab	deeplab	NOUN
fcis-19688	60	15	and	and	CCONJ
fcis-19688	60	16	densenet	densenet	NOUN
fcis-19688	60	17	models	model	NOUN
fcis-19688	60	18	in	in	ADP
fcis-19688	60	19	different	different	ADJ
fcis-19688	60	20	categories	category	NOUN
fcis-19688	60	21	.	.	PUNCT
fcis-19688	61	1	overall	overall	ADV
fcis-19688	61	2	,	,	PUNCT
fcis-19688	61	3	the	the	DET
fcis-19688	61	4	prediction	prediction	NOUN
fcis-19688	61	5	accuracy	accuracy	NOUN
fcis-19688	61	6	and	and	CCONJ
fcis-19688	61	7	errors	error	NOUN
fcis-19688	61	8	of	of	ADP
fcis-19688	61	9	the	the	DET
fcis-19688	61	10	three	three	NUM
fcis-19688	61	11	models	model	NOUN
fcis-19688	61	12	are	be	AUX
fcis-19688	61	13	slightly	slightly	ADV
fcis-19688	61	14	different	different	ADJ
fcis-19688	61	15	in	in	ADP
fcis-19688	61	16	different	different	ADJ
fcis-19688	61	17	categories	category	NOUN
fcis-19688	61	18	,	,	PUNCT
fcis-19688	61	19	but	but	CCONJ
fcis-19688	61	20	they	they	PRON
fcis-19688	61	21	all	all	PRON
fcis-19688	61	22	show	show	VERB
fcis-19688	61	23	good	good	ADJ
fcis-19688	61	24	classification	classification	NOUN
fcis-19688	61	25	ability	ability	NOUN
fcis-19688	61	26	.	.	PUNCT
fcis-19688	62	1	the	the	DET
fcis-19688	62	2	prediction	prediction	NOUN
fcis-19688	62	3	accuracy	accuracy	NOUN
fcis-19688	62	4	of	of	ADP
fcis-19688	62	5	u	u	ADJ
fcis-19688	62	6	-	-	ADJ
fcis-19688	62	7	net	net	ADJ
fcis-19688	62	8	model	model	NOUN
fcis-19688	62	9	on	on	ADP
fcis-19688	62	10	class	class	NOUN
fcis-19688	62	11	0	0	NUM
fcis-19688	62	12	and	and	CCONJ
fcis-19688	62	13	class	class	NOUN
fcis-19688	62	14	3	3	NUM
fcis-19688	62	15	is	be	AUX
fcis-19688	62	16	high	high	ADJ
fcis-19688	62	17	,	,	PUNCT
fcis-19688	62	18	and	and	CCONJ
fcis-19688	62	19	the	the	DET
fcis-19688	62	20	prediction	prediction	NOUN
fcis-19688	62	21	results	result	NOUN
fcis-19688	62	22	of	of	ADP
fcis-19688	62	23	these	these	DET
fcis-19688	62	24	two	two	NUM
fcis-19688	62	25	categories	category	NOUN
fcis-19688	62	26	are	be	AUX
fcis-19688	62	27	good	good	ADJ
fcis-19688	62	28	,	,	PUNCT
fcis-19688	62	29	which	which	PRON
fcis-19688	62	30	are	be	AUX
fcis-19688	62	31	50	50	NUM
fcis-19688	62	32	and	and	CCONJ
fcis-19688	62	33	47	47	NUM
fcis-19688	62	34	respectively	respectively	ADV
fcis-19688	62	35	.	.	PUNCT
fcis-19688	63	1	on	on	ADP
fcis-19688	63	2	class	class	NOUN
fcis-19688	63	3	1	1	NUM
fcis-19688	63	4	and	and	CCONJ
fcis-19688	63	5	class	class	NOUN
fcis-19688	63	6	2	2	NUM
fcis-19688	63	7	,	,	PUNCT
fcis-19688	63	8	the	the	DET
fcis-19688	63	9	prediction	prediction	NOUN
fcis-19688	63	10	accuracy	accuracy	NOUN
fcis-19688	63	11	of	of	ADP
fcis-19688	63	12	u	u	ADJ
fcis-19688	63	13	-	-	ADJ
fcis-19688	63	14	net	net	ADJ
fcis-19688	63	15	model	model	NOUN
fcis-19688	63	16	is	be	AUX
fcis-19688	63	17	slightly	slightly	ADV
fcis-19688	63	18	lower	low	ADJ
fcis-19688	63	19	,	,	PUNCT
fcis-19688	63	20	45	45	NUM
fcis-19688	63	21	and	and	CCONJ
fcis-19688	63	22	40	40	NUM
fcis-19688	63	23	respectively	respectively	ADV
fcis-19688	63	24	,	,	PUNCT
fcis-19688	63	25	but	but	CCONJ
fcis-19688	63	26	it	it	PRON
fcis-19688	63	27	still	still	ADV
fcis-19688	63	28	remains	remain	VERB
fcis-19688	63	29	at	at	ADP
fcis-19688	63	30	a	a	DET
fcis-19688	63	31	high	high	ADJ
fcis-19688	63	32	level	level	NOUN
fcis-19688	63	33	.	.	PUNCT
fcis-19688	64	1	the	the	DET
fcis-19688	64	2	u	u	ADJ
fcis-19688	64	3	-	-	ADJ
fcis-19688	64	4	net	net	ADJ
fcis-19688	64	5	model	model	NOUN
fcis-19688	64	6	made	make	VERB
fcis-19688	64	7	a	a	DET
fcis-19688	64	8	few	few	ADJ
fcis-19688	64	9	mistakes	mistake	NOUN
fcis-19688	64	10	in	in	ADP
fcis-19688	64	11	predicting	predict	VERB
fcis-19688	64	12	class	class	NOUN
fcis-19688	64	13	0	0	NUM
fcis-19688	64	14	,	,	PUNCT
fcis-19688	64	15	and	and	CCONJ
fcis-19688	64	16	misclassified	misclassifie	VERB
fcis-19688	64	17	five	five	NUM
fcis-19688	64	18	samples	sample	NOUN
fcis-19688	64	19	as	as	ADP
fcis-19688	64	20	class	class	NOUN
fcis-19688	64	21	1	1	NUM
fcis-19688	64	22	,	,	PUNCT
fcis-19688	64	23	but	but	CCONJ
fcis-19688	64	24	the	the	DET
fcis-19688	64	25	overall	overall	ADJ
fcis-19688	64	26	performance	performance	NOUN
fcis-19688	64	27	was	be	AUX
fcis-19688	64	28	still	still	ADV
fcis-19688	64	29	good	good	ADJ
fcis-19688	64	30	.	.	PUNCT
fcis-19688	65	1	the	the	DET
fcis-19688	65	2	prediction	prediction	NOUN
fcis-19688	65	3	accuracy	accuracy	NOUN
fcis-19688	65	4	of	of	ADP
fcis-19688	65	5	deeplab	deeplab	PROPN
fcis-19688	65	6	model	model	NOUN
fcis-19688	65	7	on	on	ADP
fcis-19688	65	8	class	class	NOUN
fcis-19688	65	9	0	0	NUM
fcis-19688	65	10	,	,	PUNCT
fcis-19688	65	11	class	class	NOUN
fcis-19688	65	12	1	1	NUM
fcis-19688	65	13	and	and	CCONJ
fcis-19688	65	14	class	class	NOUN
fcis-19688	65	15	3	3	NUM
fcis-19688	65	16	is	be	AUX
fcis-19688	65	17	relatively	relatively	ADV
fcis-19688	65	18	high	high	ADJ
fcis-19688	65	19	,	,	PUNCT
fcis-19688	65	20	48	48	NUM
fcis-19688	65	21	,	,	PUNCT
fcis-19688	65	22	43	43	NUM
fcis-19688	65	23	and	and	CCONJ
fcis-19688	65	24	46	46	NUM
fcis-19688	65	25	respectively	respectively	ADV
fcis-19688	65	26	,	,	PUNCT
fcis-19688	65	27	and	and	CCONJ
fcis-19688	65	28	the	the	DET
fcis-19688	65	29	performance	performance	NOUN
fcis-19688	65	30	is	be	AUX
fcis-19688	65	31	relatively	relatively	ADV
fcis-19688	65	32	stable	stable	ADJ
fcis-19688	65	33	.	.	PUNCT
fcis-19688	66	1	on	on	ADP
fcis-19688	66	2	class	class	NOUN
fcis-19688	66	3	2	2	NUM
fcis-19688	66	4	,	,	PUNCT
fcis-19688	66	5	the	the	DET
fcis-19688	66	6	prediction	prediction	NOUN
fcis-19688	66	7	accuracy	accuracy	NOUN
fcis-19688	66	8	of	of	ADP
fcis-19688	66	9	deeplab	deeplab	NOUN
fcis-19688	66	10	model	model	NOUN
fcis-19688	66	11	is	be	AUX
fcis-19688	66	12	slightly	slightly	ADV
fcis-19688	66	13	lower	low	ADJ
fcis-19688	66	14	,	,	PUNCT
fcis-19688	66	15	which	which	PRON
fcis-19688	66	16	is	be	AUX
fcis-19688	66	17	38	38	NUM
fcis-19688	66	18	,	,	PUNCT
fcis-19688	66	19	but	but	CCONJ
fcis-19688	66	20	the	the	DET
fcis-19688	66	21	overall	overall	ADJ
fcis-19688	66	22	performance	performance	NOUN
fcis-19688	66	23	is	be	AUX
fcis-19688	66	24	still	still	ADV
fcis-19688	66	25	satisfactory	satisfactory	ADJ
fcis-19688	66	26	.	.	PUNCT
fcis-19688	67	1	there	there	PRON
fcis-19688	67	2	are	be	VERB
fcis-19688	67	3	some	some	DET
fcis-19688	67	4	mistakes	mistake	NOUN
fcis-19688	67	5	in	in	ADP
fcis-19688	67	6	the	the	DET
fcis-19688	67	7	prediction	prediction	NOUN
fcis-19688	67	8	of	of	ADP
fcis-19688	67	9	class	class	NOUN
fcis-19688	67	10	1	1	NUM
fcis-19688	67	11	in	in	ADP
fcis-19688	67	12	the	the	DET
fcis-19688	67	13	deeplab	deeplab	NOUN
fcis-19688	67	14	model	model	NOUN
fcis-19688	67	15	,	,	PUNCT
fcis-19688	67	16	and	and	CCONJ
fcis-19688	67	17	eight	eight	NUM
fcis-19688	67	18	samples	sample	NOUN
fcis-19688	67	19	are	be	AUX
fcis-19688	67	20	wrongly	wrongly	ADV
fcis-19688	67	21	classified	classify	VERB
fcis-19688	67	22	as	as	ADP
fcis-19688	67	23	class	class	NOUN
fcis-19688	67	24	2	2	NUM
fcis-19688	67	25	,	,	PUNCT
fcis-19688	67	26	but	but	CCONJ
fcis-19688	67	27	the	the	DET
fcis-19688	67	28	prediction	prediction	NOUN
fcis-19688	67	29	effect	effect	NOUN
fcis-19688	67	30	is	be	AUX
fcis-19688	67	31	good	good	ADJ
fcis-19688	67	32	for	for	ADP
fcis-19688	67	33	other	other	ADJ
fcis-19688	67	34	69	69	NUM
fcis-19688	67	35	categories	category	NOUN
fcis-19688	67	36	.	.	PUNCT
fcis-19688	68	1	figure	figure	NOUN
fcis-19688	68	2	1	1	NUM
fcis-19688	68	3	.	.	PUNCT
fcis-19688	69	1	the	the	DET
fcis-19688	69	2	loss	loss	NOUN
fcis-19688	69	3	changes	change	NOUN
fcis-19688	69	4	of	of	ADP
fcis-19688	69	5	the	the	DET
fcis-19688	69	6	model	model	NOUN
fcis-19688	69	7	in	in	ADP
fcis-19688	69	8	training	training	NOUN
fcis-19688	69	9	set	set	NOUN
fcis-19688	69	10	and	and	CCONJ
fcis-19688	69	11	verification	verification	NOUN
fcis-19688	69	12	set	set	VERB
fcis-19688	69	13	figure	figure	NOUN
fcis-19688	69	14	2	2	NUM
fcis-19688	69	15	.	.	PUNCT
fcis-19688	69	16	prediction	prediction	NOUN
fcis-19688	69	17	accuracy	accuracy	NOUN
fcis-19688	69	18	and	and	CCONJ
fcis-19688	69	19	error	error	NOUN
fcis-19688	69	20	of	of	ADP
fcis-19688	69	21	different	different	ADJ
fcis-19688	69	22	models	model	NOUN
fcis-19688	69	23	the	the	DET
fcis-19688	69	24	prediction	prediction	NOUN
fcis-19688	69	25	accuracy	accuracy	NOUN
fcis-19688	69	26	of	of	ADP
fcis-19688	69	27	densenet	densenet	NOUN
fcis-19688	69	28	model	model	NOUN
fcis-19688	69	29	in	in	ADP
fcis-19688	69	30	all	all	DET
fcis-19688	69	31	categories	category	NOUN
fcis-19688	69	32	is	be	AUX
fcis-19688	69	33	maintained	maintain	VERB
fcis-19688	69	34	at	at	ADP
fcis-19688	69	35	a	a	DET
fcis-19688	69	36	high	high	ADJ
fcis-19688	69	37	level	level	NOUN
fcis-19688	69	38	,	,	PUNCT
fcis-19688	69	39	which	which	PRON
fcis-19688	69	40	is	be	AUX
fcis-19688	69	41	49	49	NUM
fcis-19688	69	42	,	,	PUNCT
fcis-19688	69	43	46	46	NUM
fcis-19688	69	44	,	,	PUNCT
fcis-19688	69	45	41	41	NUM
fcis-19688	69	46	and	and	CCONJ
fcis-19688	69	47	47	47	NUM
fcis-19688	69	48	respectively	respectively	ADV
fcis-19688	69	49	,	,	PUNCT
fcis-19688	69	50	and	and	CCONJ
fcis-19688	69	51	the	the	DET
fcis-19688	69	52	performance	performance	NOUN
fcis-19688	69	53	is	be	AUX
fcis-19688	69	54	stable	stable	ADJ
fcis-19688	69	55	and	and	CCONJ
fcis-19688	69	56	uniform	uniform	ADJ
fcis-19688	69	57	.	.	PUNCT
fcis-19688	70	1	in	in	ADP
fcis-19688	70	2	all	all	DET
fcis-19688	70	3	categories	category	NOUN
fcis-19688	70	4	,	,	PUNCT
fcis-19688	70	5	the	the	DET
fcis-19688	70	6	number	number	NOUN
fcis-19688	70	7	of	of	ADP
fcis-19688	70	8	false	false	ADJ
fcis-19688	70	9	predictions	prediction	NOUN
fcis-19688	70	10	of	of	ADP
fcis-19688	70	11	densenet	densenet	NOUN
fcis-19688	70	12	model	model	NOUN
fcis-19688	70	13	is	be	AUX
fcis-19688	70	14	relatively	relatively	ADV
fcis-19688	70	15	small	small	ADJ
fcis-19688	70	16	,	,	PUNCT
fcis-19688	70	17	and	and	CCONJ
fcis-19688	70	18	it	it	PRON
fcis-19688	70	19	shows	show	VERB
fcis-19688	70	20	better	well	ADJ
fcis-19688	70	21	prediction	prediction	NOUN
fcis-19688	70	22	ability	ability	NOUN
fcis-19688	70	23	as	as	ADP
fcis-19688	70	24	a	a	DET
fcis-19688	70	25	whole	whole	NOUN
fcis-19688	70	26	.	.	PUNCT
fcis-19688	71	1	densenet	densenet	NOUN
fcis-19688	71	2	model	model	NOUN
fcis-19688	71	3	has	have	VERB
fcis-19688	71	4	a	a	DET
fcis-19688	71	5	good	good	ADJ
fcis-19688	71	6	ability	ability	NOUN
fcis-19688	71	7	to	to	PART
fcis-19688	71	8	distinguish	distinguish	VERB
fcis-19688	71	9	different	different	ADJ
fcis-19688	71	10	types	type	NOUN
fcis-19688	71	11	of	of	ADP
fcis-19688	71	12	samples	sample	NOUN
fcis-19688	71	13	and	and	CCONJ
fcis-19688	71	14	accurately	accurately	ADV
fcis-19688	71	15	predicts	predict	VERB
fcis-19688	71	16	the	the	DET
fcis-19688	71	17	categories	category	NOUN
fcis-19688	71	18	of	of	ADP
fcis-19688	71	19	most	most	ADJ
fcis-19688	71	20	samples	sample	NOUN
fcis-19688	71	21	.	.	PUNCT
fcis-19688	72	1	by	by	ADP
fcis-19688	72	2	comparing	compare	VERB
fcis-19688	72	3	roc	roc	PROPN
fcis-19688	72	4	curve	curve	NOUN
fcis-19688	72	5	with	with	ADP
fcis-19688	72	6	auc	auc	NOUN
fcis-19688	72	7	value	value	NOUN
fcis-19688	72	8	,	,	PUNCT
fcis-19688	72	9	we	we	PRON
fcis-19688	72	10	can	can	AUX
fcis-19688	72	11	qualitatively	qualitatively	ADV
fcis-19688	72	12	analyze	analyze	VERB
fcis-19688	72	13	the	the	DET
fcis-19688	72	14	performance	performance	NOUN
fcis-19688	72	15	of	of	ADP
fcis-19688	72	16	u	u	NOUN
fcis-19688	72	17	-	-	NOUN
fcis-19688	72	18	net	net	ADJ
fcis-19688	72	19	,	,	PUNCT
fcis-19688	72	20	deeplab	deeplab	NOUN
fcis-19688	72	21	and	and	CCONJ
fcis-19688	72	22	densenet	densenet	NOUN
fcis-19688	72	23	models	model	NOUN
fcis-19688	72	24	in	in	ADP
fcis-19688	72	25	medical	medical	ADJ
fcis-19688	72	26	image	image	NOUN
fcis-19688	72	27	processing	processing	NOUN
fcis-19688	72	28	.	.	PUNCT
fcis-19688	73	1	figure	figure	NOUN
fcis-19688	73	2	3	3	NUM
fcis-19688	73	3	shows	show	VERB
fcis-19688	73	4	the	the	DET
fcis-19688	73	5	performance	performance	NOUN
fcis-19688	73	6	of	of	ADP
fcis-19688	73	7	u	u	NOUN
fcis-19688	73	8	-	-	NOUN
fcis-19688	73	9	net	net	ADJ
fcis-19688	73	10	,	,	PUNCT
fcis-19688	73	11	deeplab	deeplab	NOUN
fcis-19688	73	12	and	and	CCONJ
fcis-19688	73	13	densenet	densenet	NOUN
fcis-19688	73	14	models	model	NOUN
fcis-19688	73	15	in	in	ADP
fcis-19688	73	16	medical	medical	ADJ
fcis-19688	73	17	image	image	NOUN
fcis-19688	73	18	processing	processing	NOUN
fcis-19688	73	19	.	.	PUNCT
fcis-19688	74	1	figure	figure	NOUN
fcis-19688	74	2	3	3	NUM
fcis-19688	74	3	.	.	PUNCT
fcis-19688	75	1	performance	performance	NOUN
fcis-19688	75	2	of	of	ADP
fcis-19688	75	3	different	different	ADJ
fcis-19688	75	4	models	model	NOUN
fcis-19688	75	5	in	in	ADP
fcis-19688	75	6	medical	medical	ADJ
fcis-19688	75	7	image	image	NOUN
fcis-19688	75	8	processing	process	VERB
fcis-19688	75	9	70	70	NUM
fcis-19688	75	10	the	the	DET
fcis-19688	75	11	u	u	ADJ
fcis-19688	75	12	-	-	ADJ
fcis-19688	75	13	net	net	ADJ
fcis-19688	75	14	model	model	NOUN
fcis-19688	75	15	presents	present	VERB
fcis-19688	75	16	a	a	DET
fcis-19688	75	17	high	high	ADJ
fcis-19688	75	18	auc	auc	NOUN
fcis-19688	75	19	value	value	NOUN
fcis-19688	75	20	in	in	ADP
fcis-19688	75	21	the	the	DET
fcis-19688	75	22	roc	roc	PROPN
fcis-19688	75	23	curve	curve	NOUN
fcis-19688	75	24	,	,	PUNCT
fcis-19688	75	25	which	which	PRON
fcis-19688	75	26	shows	show	VERB
fcis-19688	75	27	that	that	SCONJ
fcis-19688	75	28	it	it	PRON
fcis-19688	75	29	has	have	VERB
fcis-19688	75	30	a	a	DET
fcis-19688	75	31	good	good	ADJ
fcis-19688	75	32	performance	performance	NOUN
fcis-19688	75	33	in	in	ADP
fcis-19688	75	34	the	the	DET
fcis-19688	75	35	binary	binary	ADJ
fcis-19688	75	36	classification	classification	NOUN
fcis-19688	75	37	problem	problem	NOUN
fcis-19688	75	38	and	and	CCONJ
fcis-19688	75	39	can	can	AUX
fcis-19688	75	40	effectively	effectively	ADV
fcis-19688	75	41	distinguish	distinguish	VERB
fcis-19688	75	42	positive	positive	ADJ
fcis-19688	75	43	and	and	CCONJ
fcis-19688	75	44	negative	negative	ADJ
fcis-19688	75	45	samples	sample	NOUN
fcis-19688	75	46	.	.	PUNCT
fcis-19688	76	1	although	although	SCONJ
fcis-19688	76	2	the	the	DET
fcis-19688	76	3	auc	auc	NOUN
fcis-19688	76	4	value	value	NOUN
fcis-19688	76	5	is	be	AUX
fcis-19688	76	6	high	high	ADJ
fcis-19688	76	7	,	,	PUNCT
fcis-19688	76	8	the	the	DET
fcis-19688	76	9	slope	slope	NOUN
fcis-19688	76	10	of	of	ADP
fcis-19688	76	11	roc	roc	PROPN
fcis-19688	76	12	curve	curve	NOUN
fcis-19688	76	13	is	be	AUX
fcis-19688	76	14	gentle	gentle	ADJ
fcis-19688	76	15	,	,	PUNCT
fcis-19688	76	16	which	which	PRON
fcis-19688	76	17	shows	show	VERB
fcis-19688	76	18	that	that	SCONJ
fcis-19688	76	19	the	the	DET
fcis-19688	76	20	false	false	ADJ
fcis-19688	76	21	positive	positive	ADJ
fcis-19688	76	22	rate	rate	NOUN
fcis-19688	76	23	of	of	ADP
fcis-19688	76	24	u	u	ADJ
fcis-19688	76	25	-	-	ADJ
fcis-19688	76	26	net	net	ADJ
fcis-19688	76	27	model	model	NOUN
fcis-19688	76	28	is	be	AUX
fcis-19688	76	29	still	still	ADV
fcis-19688	76	30	high	high	ADJ
fcis-19688	76	31	at	at	ADP
fcis-19688	76	32	the	the	DET
fcis-19688	76	33	expense	expense	NOUN
fcis-19688	76	34	of	of	ADP
fcis-19688	76	35	a	a	DET
fcis-19688	76	36	certain	certain	ADJ
fcis-19688	76	37	true	true	ADJ
fcis-19688	76	38	positive	positive	ADJ
fcis-19688	76	39	rate	rate	NOUN
fcis-19688	76	40	.	.	PUNCT
fcis-19688	77	1	the	the	DET
fcis-19688	77	2	auc	auc	NOUN
fcis-19688	77	3	value	value	NOUN
fcis-19688	77	4	in	in	ADP
fcis-19688	77	5	the	the	DET
fcis-19688	77	6	roc	roc	PROPN
fcis-19688	77	7	curve	curve	NOUN
fcis-19688	77	8	of	of	ADP
fcis-19688	77	9	deeplab	deeplab	PROPN
fcis-19688	77	10	model	model	NOUN
fcis-19688	77	11	is	be	AUX
fcis-19688	77	12	in	in	ADP
fcis-19688	77	13	the	the	DET
fcis-19688	77	14	middle	middle	ADJ
fcis-19688	77	15	level	level	NOUN
fcis-19688	77	16	,	,	PUNCT
fcis-19688	77	17	which	which	PRON
fcis-19688	77	18	shows	show	VERB
fcis-19688	77	19	that	that	SCONJ
fcis-19688	77	20	its	its	PRON
fcis-19688	77	21	performance	performance	NOUN
fcis-19688	77	22	on	on	ADP
fcis-19688	77	23	binary	binary	ADJ
fcis-19688	77	24	classification	classification	NOUN
fcis-19688	77	25	problems	problem	NOUN
fcis-19688	77	26	is	be	AUX
fcis-19688	77	27	average	average	ADJ
fcis-19688	77	28	and	and	CCONJ
fcis-19688	77	29	its	its	PRON
fcis-19688	77	30	prediction	prediction	NOUN
fcis-19688	77	31	accuracy	accuracy	NOUN
fcis-19688	77	32	is	be	AUX
fcis-19688	77	33	average	average	ADJ
fcis-19688	77	34	.	.	PUNCT
fcis-19688	78	1	the	the	DET
fcis-19688	78	2	slope	slope	NOUN
fcis-19688	78	3	of	of	ADP
fcis-19688	78	4	roc	roc	PROPN
fcis-19688	78	5	curve	curve	NOUN
fcis-19688	78	6	is	be	AUX
fcis-19688	78	7	relatively	relatively	ADV
fcis-19688	78	8	gentle	gentle	ADJ
fcis-19688	78	9	,	,	PUNCT
fcis-19688	78	10	which	which	PRON
fcis-19688	78	11	is	be	AUX
fcis-19688	78	12	similar	similar	ADJ
fcis-19688	78	13	to	to	ADP
fcis-19688	78	14	u	u	NOUN
fcis-19688	78	15	-	-	NOUN
fcis-19688	78	16	net	net	ADJ
fcis-19688	78	17	,	,	PUNCT
fcis-19688	78	18	indicating	indicate	VERB
fcis-19688	78	19	that	that	SCONJ
fcis-19688	78	20	the	the	DET
fcis-19688	78	21	false	false	ADJ
fcis-19688	78	22	positive	positive	ADJ
fcis-19688	78	23	rate	rate	NOUN
fcis-19688	78	24	of	of	ADP
fcis-19688	78	25	deeplab	deeplab	NOUN
fcis-19688	78	26	model	model	NOUN
fcis-19688	78	27	changes	change	VERB
fcis-19688	78	28	greatly	greatly	ADV
fcis-19688	78	29	under	under	ADP
fcis-19688	78	30	different	different	ADJ
fcis-19688	78	31	true	true	ADJ
fcis-19688	78	32	positive	positive	ADJ
fcis-19688	78	33	rates	rate	NOUN
fcis-19688	78	34	.	.	PUNCT
fcis-19688	79	1	the	the	DET
fcis-19688	79	2	roc	roc	PROPN
fcis-19688	79	3	curve	curve	NOUN
fcis-19688	79	4	of	of	ADP
fcis-19688	79	5	densenet	densenet	NOUN
fcis-19688	79	6	model	model	NOUN
fcis-19688	79	7	shows	show	VERB
fcis-19688	79	8	a	a	DET
fcis-19688	79	9	high	high	ADJ
fcis-19688	79	10	auc	auc	NOUN
fcis-19688	79	11	value	value	NOUN
fcis-19688	79	12	,	,	PUNCT
fcis-19688	79	13	which	which	PRON
fcis-19688	79	14	shows	show	VERB
fcis-19688	79	15	that	that	SCONJ
fcis-19688	79	16	it	it	PRON
fcis-19688	79	17	has	have	VERB
fcis-19688	79	18	a	a	DET
fcis-19688	79	19	good	good	ADJ
fcis-19688	79	20	performance	performance	NOUN
fcis-19688	79	21	in	in	ADP
fcis-19688	79	22	binary	binary	ADJ
fcis-19688	79	23	classification	classification	NOUN
fcis-19688	79	24	and	and	CCONJ
fcis-19688	79	25	can	can	AUX
fcis-19688	79	26	effectively	effectively	ADV
fcis-19688	79	27	distinguish	distinguish	VERB
fcis-19688	79	28	positive	positive	ADJ
fcis-19688	79	29	and	and	CCONJ
fcis-19688	79	30	negative	negative	ADJ
fcis-19688	79	31	samples	sample	NOUN
fcis-19688	79	32	.	.	PUNCT
fcis-19688	80	1	the	the	DET
fcis-19688	80	2	slope	slope	NOUN
fcis-19688	80	3	of	of	ADP
fcis-19688	80	4	roc	roc	PROPN
fcis-19688	80	5	curve	curve	NOUN
fcis-19688	80	6	is	be	AUX
fcis-19688	80	7	relatively	relatively	ADV
fcis-19688	80	8	steep	steep	ADJ
fcis-19688	80	9	,	,	PUNCT
fcis-19688	80	10	which	which	PRON
fcis-19688	80	11	shows	show	VERB
fcis-19688	80	12	that	that	SCONJ
fcis-19688	80	13	the	the	DET
fcis-19688	80	14	false	false	ADJ
fcis-19688	80	15	positive	positive	ADJ
fcis-19688	80	16	rate	rate	NOUN
fcis-19688	80	17	of	of	ADP
fcis-19688	80	18	densenet	densenet	NOUN
fcis-19688	80	19	model	model	NOUN
fcis-19688	80	20	is	be	AUX
fcis-19688	80	21	lower	low	ADJ
fcis-19688	80	22	at	at	ADP
fcis-19688	80	23	a	a	DET
fcis-19688	80	24	higher	high	ADJ
fcis-19688	80	25	true	true	ADJ
fcis-19688	80	26	positive	positive	ADJ
fcis-19688	80	27	rate	rate	NOUN
fcis-19688	80	28	.	.	PUNCT
fcis-19688	81	1	on	on	ADP
fcis-19688	81	2	the	the	DET
fcis-19688	81	3	whole	whole	NOUN
fcis-19688	81	4	,	,	PUNCT
fcis-19688	81	5	the	the	DET
fcis-19688	81	6	performance	performance	NOUN
fcis-19688	81	7	of	of	ADP
fcis-19688	81	8	densenet	densenet	NOUN
fcis-19688	81	9	model	model	NOUN
fcis-19688	81	10	in	in	ADP
fcis-19688	81	11	medical	medical	ADJ
fcis-19688	81	12	image	image	NOUN
fcis-19688	81	13	processing	processing	NOUN
fcis-19688	81	14	is	be	AUX
fcis-19688	81	15	the	the	DET
fcis-19688	81	16	best	good	ADJ
fcis-19688	81	17	,	,	PUNCT
fcis-19688	81	18	followed	follow	VERB
fcis-19688	81	19	by	by	ADP
fcis-19688	81	20	u	u	NOUN
fcis-19688	81	21	-	-	ADJ
fcis-19688	81	22	net	net	ADJ
fcis-19688	81	23	model	model	NOUN
fcis-19688	81	24	,	,	PUNCT
fcis-19688	81	25	and	and	CCONJ
fcis-19688	81	26	the	the	DET
fcis-19688	81	27	performance	performance	NOUN
fcis-19688	81	28	of	of	ADP
fcis-19688	81	29	deeplab	deeplab	PROPN
fcis-19688	81	30	model	model	NOUN
fcis-19688	81	31	is	be	AUX
fcis-19688	81	32	a	a	DET
fcis-19688	81	33	little	little	ADJ
fcis-19688	81	34	general	general	NOUN
fcis-19688	81	35	.	.	PUNCT
fcis-19688	82	1	therefore	therefore	ADV
fcis-19688	82	2	,	,	PUNCT
fcis-19688	82	3	in	in	ADP
fcis-19688	82	4	practical	practical	ADJ
fcis-19688	82	5	application	application	NOUN
fcis-19688	82	6	,	,	PUNCT
fcis-19688	82	7	we	we	PRON
fcis-19688	82	8	can	can	AUX
fcis-19688	82	9	choose	choose	VERB
fcis-19688	82	10	the	the	DET
fcis-19688	82	11	appropriate	appropriate	ADJ
fcis-19688	82	12	model	model	NOUN
fcis-19688	82	13	according	accord	VERB
fcis-19688	82	14	to	to	ADP
fcis-19688	82	15	the	the	DET
fcis-19688	82	16	specific	specific	ADJ
fcis-19688	82	17	needs	need	NOUN
fcis-19688	82	18	and	and	CCONJ
fcis-19688	82	19	tasks	task	NOUN
fcis-19688	82	20	.	.	PUNCT
fcis-19688	83	1	if	if	SCONJ
fcis-19688	83	2	high	high	ADJ
fcis-19688	83	3	-	-	PUNCT
fcis-19688	83	4	precision	precision	NOUN
fcis-19688	83	5	segmentation	segmentation	NOUN
fcis-19688	83	6	results	result	NOUN
fcis-19688	83	7	are	be	AUX
fcis-19688	83	8	needed	need	VERB
fcis-19688	83	9	,	,	PUNCT
fcis-19688	83	10	densenet	densenet	NOUN
fcis-19688	83	11	model	model	NOUN
fcis-19688	83	12	can	can	AUX
fcis-19688	83	13	be	be	AUX
fcis-19688	83	14	considered	consider	VERB
fcis-19688	83	15	.	.	PUNCT
fcis-19688	84	1	if	if	SCONJ
fcis-19688	84	2	the	the	DET
fcis-19688	84	3	performance	performance	NOUN
fcis-19688	84	4	requirements	requirement	NOUN
fcis-19688	84	5	are	be	AUX
fcis-19688	84	6	loose	loose	ADJ
fcis-19688	84	7	,	,	PUNCT
fcis-19688	84	8	you	you	PRON
fcis-19688	84	9	can	can	AUX
fcis-19688	84	10	choose	choose	VERB
fcis-19688	84	11	u	u	ADJ
fcis-19688	84	12	-	-	ADJ
fcis-19688	84	13	net	net	ADJ
fcis-19688	84	14	model	model	NOUN
fcis-19688	84	15	;	;	PUNCT
fcis-19688	84	16	the	the	DET
fcis-19688	84	17	deeplab	deeplab	NOUN
fcis-19688	84	18	model	model	NOUN
fcis-19688	84	19	can	can	AUX
fcis-19688	84	20	be	be	AUX
fcis-19688	84	21	used	use	VERB
fcis-19688	84	22	as	as	ADP
fcis-19688	84	23	a	a	DET
fcis-19688	84	24	compromise	compromise	NOUN
fcis-19688	84	25	choice	choice	NOUN
fcis-19688	85	1	[	[	X
fcis-19688	85	2	22	22	NUM
fcis-19688	85	3	-	-	SYM
fcis-19688	85	4	23	23	NUM
fcis-19688	85	5	]	]	PUNCT
fcis-19688	85	6	.	.	PUNCT
fcis-19688	86	1	in	in	ADP
fcis-19688	86	2	addition	addition	NOUN
fcis-19688	86	3	,	,	PUNCT
fcis-19688	86	4	according	accord	VERB
fcis-19688	86	5	to	to	ADP
fcis-19688	86	6	the	the	DET
fcis-19688	86	7	performance	performance	NOUN
fcis-19688	86	8	differences	difference	NOUN
fcis-19688	86	9	of	of	ADP
fcis-19688	86	10	different	different	ADJ
fcis-19688	86	11	models	model	NOUN
fcis-19688	86	12	,	,	PUNCT
fcis-19688	86	13	we	we	PRON
fcis-19688	86	14	can	can	AUX
fcis-19688	86	15	further	far	ADV
fcis-19688	86	16	explore	explore	VERB
fcis-19688	86	17	the	the	DET
fcis-19688	86	18	adjustment	adjustment	NOUN
fcis-19688	86	19	of	of	ADP
fcis-19688	86	20	model	model	NOUN
fcis-19688	86	21	structure	structure	NOUN
fcis-19688	86	22	and	and	CCONJ
fcis-19688	86	23	parameters	parameter	NOUN
fcis-19688	86	24	to	to	PART
fcis-19688	86	25	improve	improve	VERB
fcis-19688	86	26	performance	performance	NOUN
fcis-19688	86	27	.	.	PUNCT
fcis-19688	87	1	in	in	ADP
fcis-19688	87	2	view	view	NOUN
fcis-19688	87	3	of	of	ADP
fcis-19688	87	4	some	some	DET
fcis-19688	87	5	shortcomings	shortcoming	NOUN
fcis-19688	87	6	and	and	CCONJ
fcis-19688	87	7	limitations	limitation	NOUN
fcis-19688	87	8	of	of	ADP
fcis-19688	87	9	each	each	DET
fcis-19688	87	10	model	model	NOUN
fcis-19688	87	11	,	,	PUNCT
fcis-19688	87	12	we	we	PRON
fcis-19688	87	13	can	can	AUX
fcis-19688	87	14	further	far	ADV
fcis-19688	87	15	explore	explore	VERB
fcis-19688	87	16	the	the	DET
fcis-19688	87	17	improvement	improvement	NOUN
fcis-19688	87	18	and	and	CCONJ
fcis-19688	87	19	optimization	optimization	NOUN
fcis-19688	87	20	direction	direction	NOUN
fcis-19688	87	21	of	of	ADP
fcis-19688	87	22	the	the	DET
fcis-19688	87	23	model	model	NOUN
fcis-19688	87	24	.	.	PUNCT
fcis-19688	88	1	for	for	ADP
fcis-19688	88	2	example	example	NOUN
fcis-19688	88	3	,	,	PUNCT
fcis-19688	88	4	for	for	ADP
fcis-19688	88	5	the	the	DET
fcis-19688	88	6	problem	problem	NOUN
fcis-19688	88	7	of	of	ADP
fcis-19688	88	8	high	high	ADJ
fcis-19688	88	9	false	false	ADJ
fcis-19688	88	10	positive	positive	ADJ
fcis-19688	88	11	rate	rate	NOUN
fcis-19688	88	12	of	of	ADP
fcis-19688	88	13	u	u	NOUN
fcis-19688	88	14	-	-	ADJ
fcis-19688	88	15	net	net	ADJ
fcis-19688	88	16	model	model	NOUN
fcis-19688	88	17	,	,	PUNCT
fcis-19688	88	18	we	we	PRON
fcis-19688	88	19	can	can	AUX
fcis-19688	88	20	try	try	VERB
fcis-19688	88	21	to	to	PART
fcis-19688	88	22	introduce	introduce	VERB
fcis-19688	88	23	more	more	ADJ
fcis-19688	88	24	regularization	regularization	NOUN
fcis-19688	88	25	methods	method	NOUN
fcis-19688	88	26	or	or	CCONJ
fcis-19688	88	27	increase	increase	VERB
fcis-19688	88	28	the	the	DET
fcis-19688	88	29	diversity	diversity	NOUN
fcis-19688	88	30	of	of	ADP
fcis-19688	88	31	data	datum	NOUN
fcis-19688	88	32	sets	set	NOUN
fcis-19688	88	33	to	to	PART
fcis-19688	88	34	improve	improve	VERB
fcis-19688	88	35	the	the	DET
fcis-19688	88	36	generalization	generalization	NOUN
fcis-19688	88	37	ability	ability	NOUN
fcis-19688	88	38	of	of	ADP
fcis-19688	88	39	the	the	DET
fcis-19688	88	40	model[24	model[24	NOUN
fcis-19688	88	41	]	]	PUNCT
fcis-19688	88	42	.	.	PUNCT
fcis-19688	89	1	for	for	ADP
fcis-19688	89	2	the	the	DET
fcis-19688	89	3	problem	problem	NOUN
fcis-19688	89	4	of	of	ADP
fcis-19688	89	5	general	general	ADJ
fcis-19688	89	6	prediction	prediction	NOUN
fcis-19688	89	7	ability	ability	NOUN
fcis-19688	89	8	of	of	ADP
fcis-19688	89	9	deeplab	deeplab	PROPN
fcis-19688	89	10	model	model	PROPN
fcis-19688	89	11	,	,	PUNCT
fcis-19688	89	12	we	we	PRON
fcis-19688	89	13	can	can	AUX
fcis-19688	89	14	consider	consider	VERB
fcis-19688	89	15	adjusting	adjust	VERB
fcis-19688	89	16	the	the	DET
fcis-19688	89	17	network	network	NOUN
fcis-19688	89	18	structure	structure	NOUN
fcis-19688	89	19	or	or	CCONJ
fcis-19688	89	20	improving	improve	VERB
fcis-19688	89	21	the	the	DET
fcis-19688	89	22	loss	loss	NOUN
fcis-19688	89	23	function	function	NOUN
fcis-19688	89	24	to	to	PART
fcis-19688	89	25	improve	improve	VERB
fcis-19688	89	26	the	the	DET
fcis-19688	89	27	performance	performance	NOUN
fcis-19688	89	28	of	of	ADP
fcis-19688	89	29	the	the	DET
fcis-19688	89	30	model	model	NOUN
fcis-19688	89	31	[	[	X
fcis-19688	89	32	25	25	NUM
fcis-19688	89	33	]	]	PUNCT
fcis-19688	89	34	.	.	PUNCT
fcis-19688	90	1	in	in	ADP
fcis-19688	90	2	this	this	DET
fcis-19688	90	3	study	study	NOUN
fcis-19688	90	4	,	,	PUNCT
fcis-19688	90	5	the	the	DET
fcis-19688	90	6	selection	selection	NOUN
fcis-19688	90	7	of	of	ADP
fcis-19688	90	8	data	datum	NOUN
fcis-19688	90	9	sets	set	NOUN
fcis-19688	90	10	plays	play	VERB
fcis-19688	90	11	a	a	DET
fcis-19688	90	12	key	key	ADJ
fcis-19688	90	13	role	role	NOUN
fcis-19688	90	14	in	in	ADP
fcis-19688	90	15	the	the	DET
fcis-19688	90	16	performance	performance	NOUN
fcis-19688	90	17	of	of	ADP
fcis-19688	90	18	the	the	DET
fcis-19688	90	19	model	model	NOUN
fcis-19688	90	20	.	.	PUNCT
fcis-19688	91	1	in	in	ADP
fcis-19688	91	2	the	the	DET
fcis-19688	91	3	future	future	NOUN
fcis-19688	91	4	,	,	PUNCT
fcis-19688	91	5	we	we	PRON
fcis-19688	91	6	can	can	AUX
fcis-19688	91	7	further	far	ADV
fcis-19688	91	8	explore	explore	VERB
fcis-19688	91	9	the	the	DET
fcis-19688	91	10	influence	influence	NOUN
fcis-19688	91	11	of	of	ADP
fcis-19688	91	12	different	different	ADJ
fcis-19688	91	13	data	datum	NOUN
fcis-19688	91	14	sets	set	NOUN
fcis-19688	91	15	on	on	ADP
fcis-19688	91	16	the	the	DET
fcis-19688	91	17	performance	performance	NOUN
fcis-19688	91	18	of	of	ADP
fcis-19688	91	19	the	the	DET
fcis-19688	91	20	model	model	NOUN
fcis-19688	91	21	,	,	PUNCT
fcis-19688	91	22	and	and	CCONJ
fcis-19688	91	23	try	try	VERB
fcis-19688	91	24	to	to	PART
fcis-19688	91	25	build	build	VERB
fcis-19688	91	26	more	more	ADV
fcis-19688	91	27	abundant	abundant	ADJ
fcis-19688	91	28	and	and	CCONJ
fcis-19688	91	29	diverse	diverse	ADJ
fcis-19688	91	30	data	datum	NOUN
fcis-19688	91	31	sets	set	NOUN
fcis-19688	91	32	to	to	PART
fcis-19688	91	33	improve	improve	VERB
fcis-19688	91	34	the	the	DET
fcis-19688	91	35	generalization	generalization	NOUN
fcis-19688	91	36	ability	ability	NOUN
fcis-19688	91	37	and	and	CCONJ
fcis-19688	91	38	adaptability	adaptability	NOUN
fcis-19688	91	39	of	of	ADP
fcis-19688	91	40	the	the	DET
fcis-19688	91	41	model	model	NOUN
fcis-19688	91	42	.	.	PUNCT
fcis-19688	92	1	in	in	ADP
fcis-19688	92	2	medical	medical	ADJ
fcis-19688	92	3	image	image	NOUN
fcis-19688	92	4	processing	processing	NOUN
fcis-19688	92	5	,	,	PUNCT
fcis-19688	92	6	many	many	ADJ
fcis-19688	92	7	different	different	ADJ
fcis-19688	92	8	types	type	NOUN
fcis-19688	92	9	of	of	ADP
fcis-19688	92	10	medical	medical	ADJ
fcis-19688	92	11	image	image	NOUN
fcis-19688	92	12	data	datum	NOUN
fcis-19688	92	13	,	,	PUNCT
fcis-19688	92	14	such	such	ADJ
fcis-19688	92	15	as	as	ADP
fcis-19688	92	16	mri	mri	NOUN
fcis-19688	92	17	,	,	PUNCT
fcis-19688	92	18	ct	ct	PROPN
fcis-19688	92	19	and	and	CCONJ
fcis-19688	92	20	x	x	NOUN
fcis-19688	92	21	-	-	NOUN
fcis-19688	92	22	ray	ray	NOUN
fcis-19688	92	23	,	,	PUNCT
fcis-19688	92	24	are	be	AUX
fcis-19688	92	25	often	often	ADV
fcis-19688	92	26	involved	involve	VERB
fcis-19688	92	27	.	.	PUNCT
fcis-19688	93	1	future	future	ADJ
fcis-19688	93	2	research	research	NOUN
fcis-19688	93	3	can	can	AUX
fcis-19688	93	4	explore	explore	VERB
fcis-19688	93	5	how	how	SCONJ
fcis-19688	93	6	to	to	PART
fcis-19688	93	7	effectively	effectively	ADV
fcis-19688	93	8	fuse	fuse	VERB
fcis-19688	93	9	multimodal	multimodal	NOUN
fcis-19688	93	10	information	information	NOUN
fcis-19688	93	11	to	to	PART
fcis-19688	93	12	improve	improve	VERB
fcis-19688	93	13	the	the	DET
fcis-19688	93	14	performance	performance	NOUN
fcis-19688	93	15	and	and	CCONJ
fcis-19688	93	16	efficiency	efficiency	NOUN
fcis-19688	93	17	of	of	ADP
fcis-19688	93	18	deep	deep	ADJ
fcis-19688	93	19	learning	learning	NOUN
fcis-19688	93	20	model	model	NOUN
fcis-19688	93	21	in	in	ADP
fcis-19688	93	22	medical	medical	ADJ
fcis-19688	93	23	image	image	NOUN
fcis-19688	93	24	processing	processing	NOUN
fcis-19688	93	25	.	.	PUNCT
fcis-19688	94	1	besides	besides	SCONJ
fcis-19688	94	2	binary	binary	ADJ
fcis-19688	94	3	classification	classification	NOUN
fcis-19688	94	4	,	,	PUNCT
fcis-19688	94	5	medical	medical	ADJ
fcis-19688	94	6	image	image	NOUN
fcis-19688	94	7	processing	processing	NOUN
fcis-19688	94	8	involves	involve	VERB
fcis-19688	94	9	many	many	ADJ
fcis-19688	94	10	other	other	ADJ
fcis-19688	94	11	tasks	task	NOUN
fcis-19688	94	12	,	,	PUNCT
fcis-19688	94	13	such	such	ADJ
fcis-19688	94	14	as	as	ADP
fcis-19688	94	15	segmentation	segmentation	NOUN
fcis-19688	94	16	,	,	PUNCT
fcis-19688	94	17	detection	detection	NOUN
fcis-19688	94	18	and	and	CCONJ
fcis-19688	94	19	classification	classification	NOUN
fcis-19688	94	20	.	.	PUNCT
fcis-19688	95	1	in	in	ADP
fcis-19688	95	2	the	the	DET
fcis-19688	95	3	future	future	NOUN
fcis-19688	95	4	,	,	PUNCT
fcis-19688	95	5	the	the	DET
fcis-19688	95	6	methods	method	NOUN
fcis-19688	95	7	and	and	CCONJ
fcis-19688	95	8	techniques	technique	NOUN
fcis-19688	95	9	of	of	ADP
fcis-19688	95	10	this	this	DET
fcis-19688	95	11	study	study	NOUN
fcis-19688	95	12	can	can	AUX
fcis-19688	95	13	be	be	AUX
fcis-19688	95	14	extended	extend	VERB
fcis-19688	95	15	to	to	ADP
fcis-19688	95	16	other	other	ADJ
fcis-19688	95	17	types	type	NOUN
fcis-19688	95	18	of	of	ADP
fcis-19688	95	19	medical	medical	ADJ
fcis-19688	95	20	image	image	NOUN
fcis-19688	95	21	processing	processing	NOUN
fcis-19688	95	22	tasks	task	NOUN
fcis-19688	95	23	to	to	PART
fcis-19688	95	24	meet	meet	VERB
fcis-19688	95	25	the	the	DET
fcis-19688	95	26	needs	need	NOUN
fcis-19688	95	27	of	of	ADP
fcis-19688	95	28	different	different	ADJ
fcis-19688	95	29	application	application	NOUN
fcis-19688	95	30	scenarios	scenario	NOUN
fcis-19688	95	31	.	.	PUNCT
fcis-19688	96	1	5	5	X
fcis-19688	96	2	.	.	X
fcis-19688	96	3	conclusion	conclusion	NOUN
fcis-19688	96	4	in	in	ADP
fcis-19688	96	5	this	this	DET
fcis-19688	96	6	paper	paper	NOUN
fcis-19688	96	7	,	,	PUNCT
fcis-19688	96	8	the	the	DET
fcis-19688	96	9	deep	deep	ADJ
fcis-19688	96	10	learning	learning	NOUN
fcis-19688	96	11	algorithm	algorithm	NOUN
fcis-19688	96	12	in	in	ADP
fcis-19688	96	13	medical	medical	ADJ
fcis-19688	96	14	image	image	NOUN
fcis-19688	96	15	processing	processing	NOUN
fcis-19688	96	16	is	be	AUX
fcis-19688	96	17	deeply	deeply	ADV
fcis-19688	96	18	discussed	discuss	VERB
fcis-19688	96	19	and	and	CCONJ
fcis-19688	96	20	studied	study	VERB
fcis-19688	96	21	.	.	PUNCT
fcis-19688	97	1	by	by	ADP
fcis-19688	97	2	evaluating	evaluate	VERB
fcis-19688	97	3	and	and	CCONJ
fcis-19688	97	4	comparing	compare	VERB
fcis-19688	97	5	the	the	DET
fcis-19688	97	6	performance	performance	NOUN
fcis-19688	97	7	of	of	ADP
fcis-19688	97	8	u	u	NOUN
fcis-19688	97	9	-	-	NOUN
fcis-19688	97	10	net	net	ADJ
fcis-19688	97	11	,	,	PUNCT
fcis-19688	97	12	deeplab	deeplab	NOUN
fcis-19688	97	13	and	and	CCONJ
fcis-19688	97	14	densenet	densenet	NOUN
fcis-19688	97	15	models	model	NOUN
fcis-19688	97	16	on	on	ADP
fcis-19688	97	17	the	the	DET
fcis-19688	97	18	binary	binary	ADJ
fcis-19688	97	19	classification	classification	NOUN
fcis-19688	97	20	problem	problem	NOUN
fcis-19688	97	21	,	,	PUNCT
fcis-19688	97	22	it	it	PRON
fcis-19688	97	23	is	be	AUX
fcis-19688	97	24	found	find	VERB
fcis-19688	97	25	that	that	SCONJ
fcis-19688	97	26	densenet	densenet	NOUN
fcis-19688	97	27	model	model	NOUN
fcis-19688	97	28	has	have	VERB
fcis-19688	97	29	high	high	ADJ
fcis-19688	97	30	performance	performance	NOUN
fcis-19688	97	31	in	in	ADP
fcis-19688	97	32	medical	medical	ADJ
fcis-19688	97	33	image	image	NOUN
fcis-19688	97	34	processing	processing	NOUN
fcis-19688	97	35	,	,	PUNCT
fcis-19688	97	36	its	its	PRON
fcis-19688	97	37	auc	auc	NOUN
fcis-19688	97	38	value	value	NOUN
fcis-19688	97	39	is	be	AUX
fcis-19688	97	40	obviously	obviously	ADV
fcis-19688	97	41	higher	high	ADJ
fcis-19688	97	42	than	than	ADP
fcis-19688	97	43	0.6	0.6	NUM
fcis-19688	97	44	,	,	PUNCT
fcis-19688	97	45	and	and	CCONJ
fcis-19688	97	46	its	its	PRON
fcis-19688	97	47	prediction	prediction	NOUN
fcis-19688	97	48	accuracy	accuracy	NOUN
fcis-19688	97	49	is	be	AUX
fcis-19688	97	50	higher	high	ADJ
fcis-19688	97	51	.	.	PUNCT
fcis-19688	98	1	however	however	ADV
fcis-19688	98	2	,	,	PUNCT
fcis-19688	98	3	the	the	DET
fcis-19688	98	4	performance	performance	NOUN
fcis-19688	98	5	of	of	ADP
fcis-19688	98	6	u	u	NOUN
fcis-19688	98	7	-	-	NOUN
fcis-19688	98	8	net	net	ADJ
fcis-19688	98	9	and	and	CCONJ
fcis-19688	98	10	deeplab	deeplab	NOUN
fcis-19688	98	11	models	model	NOUN
fcis-19688	98	12	is	be	AUX
fcis-19688	98	13	a	a	DET
fcis-19688	98	14	little	little	ADJ
fcis-19688	98	15	general	general	NOUN
fcis-19688	98	16	,	,	PUNCT
fcis-19688	98	17	with	with	ADP
fcis-19688	98	18	auc	auc	NOUN
fcis-19688	98	19	value	value	NOUN
fcis-19688	98	20	around	around	ADP
fcis-19688	98	21	0.6	0.6	NUM
fcis-19688	98	22	,	,	PUNCT
fcis-19688	98	23	and	and	CCONJ
fcis-19688	98	24	the	the	DET
fcis-19688	98	25	prediction	prediction	NOUN
fcis-19688	98	26	accuracy	accuracy	NOUN
fcis-19688	98	27	is	be	AUX
fcis-19688	98	28	limited	limited	ADJ
fcis-19688	98	29	.	.	PUNCT
fcis-19688	99	1	comparing	compare	VERB
fcis-19688	99	2	the	the	DET
fcis-19688	99	3	performance	performance	NOUN
fcis-19688	99	4	of	of	ADP
fcis-19688	99	5	each	each	DET
fcis-19688	99	6	model	model	NOUN
fcis-19688	99	7	,	,	PUNCT
fcis-19688	99	8	we	we	PRON
fcis-19688	99	9	find	find	VERB
fcis-19688	99	10	that	that	SCONJ
fcis-19688	99	11	densenet	densenet	NOUN
fcis-19688	99	12	model	model	NOUN
fcis-19688	99	13	has	have	VERB
fcis-19688	99	14	good	good	ADJ
fcis-19688	99	15	generalization	generalization	NOUN
fcis-19688	99	16	ability	ability	NOUN
fcis-19688	99	17	and	and	CCONJ
fcis-19688	99	18	classification	classification	NOUN
fcis-19688	99	19	accuracy	accuracy	NOUN
fcis-19688	99	20	,	,	PUNCT
fcis-19688	99	21	which	which	PRON
fcis-19688	99	22	is	be	AUX
fcis-19688	99	23	suitable	suitable	ADJ
fcis-19688	99	24	for	for	ADP
fcis-19688	99	25	the	the	DET
fcis-19688	99	26	binary	binary	ADJ
fcis-19688	99	27	classification	classification	NOUN
fcis-19688	99	28	problem	problem	NOUN
fcis-19688	99	29	in	in	ADP
fcis-19688	99	30	medical	medical	ADJ
fcis-19688	99	31	image	image	NOUN
fcis-19688	99	32	processing	processing	NOUN
fcis-19688	99	33	.	.	PUNCT
fcis-19688	100	1	although	although	SCONJ
fcis-19688	100	2	the	the	DET
fcis-19688	100	3	prediction	prediction	NOUN
fcis-19688	100	4	accuracy	accuracy	NOUN
fcis-19688	100	5	of	of	ADP
fcis-19688	100	6	u	u	ADJ
fcis-19688	100	7	-	-	ADJ
fcis-19688	100	8	net	net	ADJ
fcis-19688	100	9	model	model	NOUN
fcis-19688	100	10	is	be	AUX
fcis-19688	100	11	slightly	slightly	ADV
fcis-19688	100	12	lower	low	ADJ
fcis-19688	100	13	,	,	PUNCT
fcis-19688	100	14	its	its	PRON
fcis-19688	100	15	network	network	NOUN
fcis-19688	100	16	structure	structure	NOUN
fcis-19688	100	17	is	be	AUX
fcis-19688	100	18	simple	simple	ADJ
fcis-19688	100	19	and	and	CCONJ
fcis-19688	100	20	its	its	PRON
fcis-19688	100	21	training	training	NOUN
fcis-19688	100	22	speed	speed	NOUN
fcis-19688	100	23	is	be	AUX
fcis-19688	100	24	faster	fast	ADJ
fcis-19688	100	25	,	,	PUNCT
fcis-19688	100	26	so	so	CCONJ
fcis-19688	100	27	it	it	PRON
fcis-19688	100	28	is	be	AUX
fcis-19688	100	29	suitable	suitable	ADJ
fcis-19688	100	30	for	for	ADP
fcis-19688	100	31	some	some	DET
fcis-19688	100	32	scenes	scene	NOUN
fcis-19688	100	33	with	with	ADP
fcis-19688	100	34	high	high	ADJ
fcis-19688	100	35	real	real	ADJ
fcis-19688	100	36	-	-	PUNCT
fcis-19688	100	37	time	time	NOUN
fcis-19688	100	38	requirements	requirement	NOUN
fcis-19688	100	39	.	.	PUNCT
fcis-19688	101	1	deeplab	deeplab	PROPN
fcis-19688	101	2	model	model	NOUN
fcis-19688	101	3	is	be	AUX
fcis-19688	101	4	in	in	ADP
fcis-19688	101	5	the	the	DET
fcis-19688	101	6	middle	middle	NOUN
fcis-19688	101	7	of	of	ADP
fcis-19688	101	8	prediction	prediction	NOUN
fcis-19688	101	9	ability	ability	NOUN
fcis-19688	101	10	,	,	PUNCT
fcis-19688	101	11	which	which	PRON
fcis-19688	101	12	has	have	VERB
fcis-19688	101	13	certain	certain	ADJ
fcis-19688	101	14	advantages	advantage	NOUN
fcis-19688	101	15	for	for	ADP
fcis-19688	101	16	fine	fine	ADV
fcis-19688	101	17	-	-	PUNCT
fcis-19688	101	18	grained	grain	VERB
fcis-19688	101	19	image	image	NOUN
fcis-19688	101	20	segmentation	segmentation	NOUN
fcis-19688	101	21	,	,	PUNCT
fcis-19688	101	22	but	but	CCONJ
fcis-19688	101	23	it	it	PRON
fcis-19688	101	24	is	be	AUX
fcis-19688	101	25	not	not	PART
fcis-19688	101	26	effective	effective	ADJ
fcis-19688	101	27	for	for	ADP
fcis-19688	101	28	large	large	ADJ
fcis-19688	101	29	-	-	PUNCT
fcis-19688	101	30	scale	scale	NOUN
fcis-19688	101	31	image	image	NOUN
fcis-19688	101	32	processing	processing	NOUN
fcis-19688	101	33	.	.	PUNCT
fcis-19688	102	1	in	in	ADP
fcis-19688	102	2	the	the	DET
fcis-19688	102	3	future	future	ADJ
fcis-19688	102	4	research	research	NOUN
fcis-19688	102	5	,	,	PUNCT
fcis-19688	102	6	we	we	PRON
fcis-19688	102	7	will	will	AUX
fcis-19688	102	8	continue	continue	VERB
fcis-19688	102	9	to	to	PART
fcis-19688	102	10	explore	explore	VERB
fcis-19688	102	11	the	the	DET
fcis-19688	102	12	optimization	optimization	NOUN
fcis-19688	102	13	and	and	CCONJ
fcis-19688	102	14	improvement	improvement	NOUN
fcis-19688	102	15	methods	method	NOUN
fcis-19688	102	16	of	of	ADP
fcis-19688	102	17	deep	deep	ADJ
fcis-19688	102	18	learning	learning	NOUN
fcis-19688	102	19	algorithm	algorithm	NOUN
fcis-19688	102	20	in	in	ADP
fcis-19688	102	21	medical	medical	ADJ
fcis-19688	102	22	image	image	NOUN
fcis-19688	102	23	processing	processing	NOUN
fcis-19688	102	24	,	,	PUNCT
fcis-19688	102	25	including	include	VERB
fcis-19688	102	26	further	further	ADJ
fcis-19688	102	27	optimization	optimization	NOUN
fcis-19688	102	28	of	of	ADP
fcis-19688	102	29	model	model	NOUN
fcis-19688	102	30	structure	structure	NOUN
fcis-19688	102	31	,	,	PUNCT
fcis-19688	102	32	fusion	fusion	NOUN
fcis-19688	102	33	of	of	ADP
fcis-19688	102	34	multimodal	multimodal	NOUN
fcis-19688	102	35	information	information	NOUN
fcis-19688	102	36	and	and	CCONJ
fcis-19688	102	37	enrichment	enrichment	NOUN
fcis-19688	102	38	and	and	CCONJ
fcis-19688	102	39	diversification	diversification	NOUN
fcis-19688	102	40	of	of	ADP
fcis-19688	102	41	data	datum	NOUN
fcis-19688	102	42	sets	set	NOUN
fcis-19688	102	43	.	.	PUNCT
fcis-19688	103	1	at	at	ADP
fcis-19688	103	2	the	the	DET
fcis-19688	103	3	same	same	ADJ
fcis-19688	103	4	time	time	NOUN
fcis-19688	103	5	,	,	PUNCT
fcis-19688	103	6	we	we	PRON
fcis-19688	103	7	will	will	AUX
fcis-19688	103	8	expand	expand	VERB
fcis-19688	103	9	the	the	DET
fcis-19688	103	10	application	application	NOUN
fcis-19688	103	11	scenarios	scenario	NOUN
fcis-19688	103	12	of	of	ADP
fcis-19688	103	13	the	the	DET
fcis-19688	103	14	research	research	NOUN
fcis-19688	103	15	and	and	CCONJ
fcis-19688	103	16	apply	apply	VERB
fcis-19688	103	17	the	the	DET
fcis-19688	103	18	deep	deep	ADJ
fcis-19688	103	19	learning	learning	NOUN
fcis-19688	103	20	algorithm	algorithm	NOUN
fcis-19688	103	21	to	to	ADP
fcis-19688	103	22	more	more	ADJ
fcis-19688	103	23	types	type	NOUN
fcis-19688	103	24	of	of	ADP
fcis-19688	103	25	medical	medical	ADJ
fcis-19688	103	26	image	image	NOUN
fcis-19688	103	27	processing	processing	NOUN
fcis-19688	103	28	tasks	task	NOUN
fcis-19688	103	29	to	to	PART
fcis-19688	103	30	improve	improve	VERB
fcis-19688	103	31	the	the	DET
fcis-19688	103	32	accuracy	accuracy	NOUN
fcis-19688	103	33	and	and	CCONJ
fcis-19688	103	34	efficiency	efficiency	NOUN
fcis-19688	103	35	of	of	ADP
fcis-19688	103	36	medical	medical	ADJ
fcis-19688	103	37	diagnosis	diagnosis	NOUN
fcis-19688	103	38	and	and	CCONJ
fcis-19688	103	39	treatment	treatment	NOUN
fcis-19688	103	40	.	.	PUNCT
fcis-19688	104	1	references	reference	NOUN
fcis-19688	104	2	[	[	X
fcis-19688	104	3	1	1	NUM
fcis-19688	104	4	]	]	X
fcis-19688	104	5	zhang	zhang	PROPN
fcis-19688	104	6	,	,	PUNCT
fcis-19688	104	7	x.	x.	PROPN
fcis-19688	104	8	,	,	PUNCT
fcis-19688	104	9	chen	chen	PROPN
fcis-19688	104	10	,	,	PUNCT
fcis-19688	104	11	z.	z.	PROPN
fcis-19688	104	12	,	,	PUNCT
fcis-19688	104	13	gao	gao	PROPN
fcis-19688	104	14	,	,	PUNCT
fcis-19688	104	15	j.	j.	PROPN
fcis-19688	104	16	,	,	PUNCT
fcis-19688	104	17	huang	huang	PROPN
fcis-19688	104	18	,	,	PUNCT
fcis-19688	104	19	w.	w.	PROPN
fcis-19688	104	20	,	,	PUNCT
fcis-19688	104	21	li	li	PROPN
fcis-19688	104	22	,	,	PUNCT
fcis-19688	104	23	p.	p.	NOUN
fcis-19688	104	24	,	,	PUNCT
fcis-19688	104	25	&	&	CCONJ
fcis-19688	104	26	zhang	zhang	PROPN
fcis-19688	104	27	,	,	PUNCT
fcis-19688	104	28	j.	j.	PROPN
fcis-19688	104	29	(	(	PUNCT
fcis-19688	104	30	2022	2022	NUM
fcis-19688	104	31	)	)	PUNCT
fcis-19688	104	32	.	.	PUNCT
fcis-19688	105	1	a	a	DET
fcis-19688	105	2	two	two	NUM
fcis-19688	105	3	-	-	PUNCT
fcis-19688	105	4	stage	stage	NOUN
fcis-19688	105	5	deep	deep	ADJ
fcis-19688	105	6	transfer	transfer	NOUN
fcis-19688	105	7	learning	learning	NOUN
fcis-19688	105	8	model	model	NOUN
fcis-19688	105	9	and	and	CCONJ
fcis-19688	105	10	its	its	PRON
fcis-19688	105	11	application	application	NOUN
fcis-19688	105	12	for	for	ADP
fcis-19688	105	13	medical	medical	ADJ
fcis-19688	105	14	image	image	NOUN
fcis-19688	105	15	processing	processing	NOUN
fcis-19688	105	16	in	in	ADP
fcis-19688	105	17	traditional	traditional	ADJ
fcis-19688	105	18	chinese	chinese	ADJ
fcis-19688	105	19	medicine	medicine	NOUN
fcis-19688	105	20	.	.	PUNCT
fcis-19688	106	1	knowledge	knowledge	NOUN
fcis-19688	106	2	-	-	PUNCT
fcis-19688	106	3	based	base	VERB
fcis-19688	106	4	systems	system	NOUN
fcis-19688	106	5	,	,	PUNCT
fcis-19688	106	6	2022(5	2022(5	NUM
fcis-19688	106	7	)	)	PUNCT
fcis-19688	106	8	,	,	PUNCT
fcis-19688	106	9	239	239	NUM
fcis-19688	106	10	.	.	PUNCT
fcis-19688	107	1	[	[	X
fcis-19688	107	2	2	2	NUM
fcis-19688	107	3	]	]	X
fcis-19688	107	4	rajendran	rajendran	NOUN
fcis-19688	107	5	,	,	PUNCT
fcis-19688	107	6	t.	t.	PROPN
fcis-19688	107	7	,	,	PUNCT
fcis-19688	107	8	sridhar	sridhar	PROPN
fcis-19688	107	9	,	,	PUNCT
fcis-19688	107	10	k.	k.	PROPN
fcis-19688	108	1	p.	p.	PROPN
fcis-19688	108	2	,	,	PUNCT
fcis-19688	108	3	manimurugan	manimurugan	NOUN
fcis-19688	108	4	,	,	PUNCT
fcis-19688	108	5	s.	s.	PROPN
fcis-19688	108	6	,	,	PUNCT
fcis-19688	108	7	&	&	CCONJ
fcis-19688	108	8	deepa	deepa	PROPN
fcis-19688	108	9	,	,	PUNCT
fcis-19688	108	10	s.	s.	PROPN
fcis-19688	108	11	(	(	PUNCT
fcis-19688	108	12	2019	2019	NUM
fcis-19688	108	13	)	)	PUNCT
fcis-19688	108	14	.	.	PUNCT
fcis-19688	109	1	advanced	advanced	ADJ
fcis-19688	109	2	algorithms	algorithm	NOUN
fcis-19688	109	3	for	for	ADP
fcis-19688	109	4	medical	medical	ADJ
fcis-19688	109	5	image	image	NOUN
fcis-19688	109	6	processing	processing	NOUN
fcis-19688	109	7	.	.	PUNCT
fcis-19688	110	1	the	the	DET
fcis-19688	110	2	open	open	ADJ
fcis-19688	110	3	biomedical	biomedical	ADJ
fcis-19688	110	4	engineering	engineering	NOUN
fcis-19688	110	5	journal	journal	NOUN
fcis-19688	110	6	,	,	PUNCT
fcis-19688	110	7	13(1	13(1	NUM
fcis-19688	110	8	)	)	PUNCT
fcis-19688	110	9	,	,	PUNCT
fcis-19688	110	10	102	102	NUM
fcis-19688	110	11	-	-	SYM
fcis-19688	110	12	102	102	NUM
fcis-19688	110	13	.	.	PUNCT
fcis-19688	111	1	[	[	X
fcis-19688	111	2	3	3	NUM
fcis-19688	111	3	]	]	SYM
fcis-19688	111	4	rajagopalan	rajagopalan	NOUN
fcis-19688	111	5	,	,	PUNCT
fcis-19688	111	6	s.	s.	PROPN
fcis-19688	111	7	,	,	PUNCT
fcis-19688	111	8	poori	poori	PROPN
fcis-19688	111	9	,	,	PUNCT
fcis-19688	111	10	s.	s.	PROPN
fcis-19688	111	11	,	,	PUNCT
fcis-19688	111	12	narasimhan	narasimhan	PROPN
fcis-19688	111	13	,	,	PUNCT
fcis-19688	111	14	m.	m.	NOUN
fcis-19688	111	15	,	,	PUNCT
fcis-19688	111	16	rethinam	rethinam	PROPN
fcis-19688	111	17	,	,	PUNCT
fcis-19688	111	18	s.	s.	PROPN
fcis-19688	111	19	,	,	PUNCT
fcis-19688	111	20	kuppusamy	kuppusamy	PROPN
fcis-19688	111	21	,	,	PUNCT
fcis-19688	111	22	c.	c.	PROPN
fcis-19688	111	23	v.	v.	PROPN
fcis-19688	111	24	,	,	PUNCT
fcis-19688	111	25	&	&	CCONJ
fcis-19688	111	26	balasubramanian	balasubramanian	PROPN
fcis-19688	111	27	,	,	PUNCT
fcis-19688	111	28	r.	r.	PROPN
fcis-19688	111	29	,	,	PUNCT
fcis-19688	111	30	et	et	PROPN
fcis-19688	111	31	al	al	PROPN
fcis-19688	111	32	.	.	PROPN
fcis-19688	112	1	(	(	PUNCT
fcis-19688	112	2	2020	2020	NUM
fcis-19688	112	3	)	)	PUNCT
fcis-19688	112	4	.	.	PUNCT
fcis-19688	113	1	chua	chua	PROPN
fcis-19688	113	2	's	's	PART
fcis-19688	113	3	diode	diode	NOUN
fcis-19688	113	4	and	and	CCONJ
fcis-19688	113	5	strange	strange	ADJ
fcis-19688	113	6	attractor	attractor	NOUN
fcis-19688	113	7	:	:	PUNCT
fcis-19688	113	8	a	a	DET
fcis-19688	113	9	three	three	NUM
fcis-19688	113	10	-	-	PUNCT
fcis-19688	113	11	layer	layer	NOUN
fcis-19688	113	12	hardware	hardware	NOUN
fcis-19688	113	13	software	software	NOUN
fcis-19688	113	14	co	co	NOUN
fcis-19688	113	15	-	-	NOUN
fcis-19688	113	16	design	design	NOUN
fcis-19688	113	17	for	for	ADP
fcis-19688	113	18	medical	medical	ADJ
fcis-19688	113	19	image	image	NOUN
fcis-19688	113	20	confidentiality	confidentiality	NOUN
fcis-19688	113	21	.	.	PUNCT
fcis-19688	114	1	iet	iet	PROPN
fcis-19688	114	2	image	image	PROPN
fcis-19688	114	3	processing	processing	NOUN
fcis-19688	114	4	,	,	PUNCT
fcis-19688	114	5	14(7	14(7	NUM
fcis-19688	114	6	)	)	PUNCT
fcis-19688	114	7	,	,	PUNCT
fcis-19688	114	8	1354	1354	NUM
fcis-19688	114	9	-	-	SYM
fcis-19688	114	10	1365	1365	NUM
fcis-19688	114	11	.	.	PUNCT
fcis-19688	115	1	[	[	X
fcis-19688	115	2	4	4	NUM
fcis-19688	115	3	]	]	X
fcis-19688	115	4	yan	yan	PROPN
fcis-19688	115	5	,	,	PUNCT
fcis-19688	115	6	x.	x.	PROPN
fcis-19688	115	7	,	,	PUNCT
fcis-19688	115	8	xiao	xiao	PROPN
fcis-19688	115	9	,	,	PUNCT
fcis-19688	115	10	m.	m.	NOUN
fcis-19688	115	11	,	,	PUNCT
fcis-19688	115	12	wang	wang	PROPN
fcis-19688	115	13	,	,	PUNCT
fcis-19688	115	14	w.	w.	PROPN
fcis-19688	115	15	,	,	PUNCT
fcis-19688	115	16	li	li	PROPN
fcis-19688	115	17	,	,	PUNCT
fcis-19688	115	18	y.	y.	PROPN
fcis-19688	115	19	,	,	PUNCT
fcis-19688	115	20	&	&	CCONJ
fcis-19688	115	21	zhang	zhang	PROPN
fcis-19688	115	22	,	,	PUNCT
fcis-19688	115	23	f.	f.	PROPN
fcis-19688	115	24	(	(	PUNCT
fcis-19688	115	25	2024	2024	NUM
fcis-19688	115	26	)	)	PUNCT
fcis-19688	115	27	.	.	PUNCT
fcis-19688	116	1	a	a	DET
fcis-19688	116	2	self	self	NOUN
fcis-19688	116	3	-	-	PUNCT
fcis-19688	116	4	guided	guide	VERB
fcis-19688	116	5	deep	deep	ADJ
fcis-19688	116	6	learning	learning	NOUN
fcis-19688	116	7	technique	technique	NOUN
fcis-19688	116	8	for	for	ADP
fcis-19688	116	9	mri	mri	NOUN
fcis-19688	116	10	image	image	NOUN
fcis-19688	116	11	noise	noise	NOUN
fcis-19688	116	12	reduction	reduction	NOUN
fcis-19688	116	13	.	.	PUNCT
fcis-19688	117	1	journal	journal	PROPN
fcis-19688	117	2	of	of	ADP
fcis-19688	117	3	theory	theory	NOUN
fcis-19688	117	4	and	and	CCONJ
fcis-19688	117	5	practice	practice	NOUN
fcis-19688	117	6	of	of	ADP
fcis-19688	117	7	engineering	engineering	NOUN
fcis-19688	117	8	science	science	NOUN
fcis-19688	117	9	,	,	PUNCT
fcis-19688	117	10	4(01	4(01	NUM
fcis-19688	117	11	)	)	PUNCT
fcis-19688	117	12	,	,	PUNCT
fcis-19688	117	13	109–117	109–117	NUM
fcis-19688	117	14	.	.	PUNCT
fcis-19688	118	1	https://doi.org/10	https://doi.org/10	PROPN
fcis-19688	118	2	.	.	PUNCT
fcis-19688	119	1	53469	53469	NUM
fcis-19688	119	2	/	/	SYM
fcis-19688	119	3	jtpes	jtpe	NOUN
fcis-19688	119	4	.	.	PUNCT
fcis-19688	120	1	2024	2024	NUM
fcis-19688	120	2	.	.	PUNCT
fcis-19688	121	1	04(01).15	04(01).15	NUM
fcis-19688	121	2	[	[	X
fcis-19688	121	3	5	5	NUM
fcis-19688	121	4	]	]	SYM
fcis-19688	121	5	xie	xie	PROPN
fcis-19688	121	6	,	,	PUNCT
fcis-19688	121	7	j.	j.	PROPN
fcis-19688	121	8	,	,	PUNCT
fcis-19688	121	9	pun	pun	PROPN
fcis-19688	121	10	,	,	PUNCT
fcis-19688	121	11	c.	c.	PROPN
fcis-19688	121	12	m.	m.	PROPN
fcis-19688	121	13	,	,	PUNCT
fcis-19688	121	14	pan	pan	PROPN
fcis-19688	121	15	,	,	PUNCT
fcis-19688	121	16	z.	z.	PROPN
fcis-19688	121	17	,	,	PUNCT
fcis-19688	121	18	gao	gao	PROPN
fcis-19688	121	19	,	,	PUNCT
fcis-19688	121	20	h.	h.	PROPN
fcis-19688	121	21	,	,	PUNCT
fcis-19688	121	22	&	&	CCONJ
fcis-19688	121	23	wang	wang	PROPN
fcis-19688	121	24	,	,	PUNCT
fcis-19688	121	25	b.	b.	PROPN
fcis-19688	121	26	(	(	PUNCT
fcis-19688	121	27	2019	2019	NUM
fcis-19688	121	28	)	)	PUNCT
fcis-19688	121	29	.	.	PUNCT
fcis-19688	122	1	automatic	automatic	ADJ
fcis-19688	122	2	medical	medical	ADJ
fcis-19688	122	3	image	image	NOUN
fcis-19688	122	4	registration	registration	NOUN
fcis-19688	122	5	based	base	VERB
fcis-19688	122	6	on	on	ADP
fcis-19688	122	7	an	an	DET
fcis-19688	122	8	integrated	integrate	VERB
fcis-19688	122	9	method	method	NOUN
fcis-19688	122	10	combining	combine	VERB
fcis-19688	122	11	feature	feature	NOUN
fcis-19688	122	12	and	and	CCONJ
fcis-19688	122	13	area	area	NOUN
fcis-19688	122	14	information	information	NOUN
fcis-19688	122	15	.	.	PUNCT
fcis-19688	123	1	neural	neural	ADJ
fcis-19688	123	2	processing	processing	NOUN
fcis-19688	123	3	letters	letter	NOUN
fcis-19688	123	4	,	,	PUNCT
fcis-19688	123	5	49(1	49(1	NOUN
fcis-19688	123	6	)	)	PUNCT
fcis-19688	123	7	,	,	PUNCT
fcis-19688	123	8	263	263	NUM
fcis-19688	123	9	-	-	SYM
fcis-19688	123	10	284	284	NUM
fcis-19688	123	11	.	.	PUNCT
fcis-19688	124	1	[	[	X
fcis-19688	124	2	6	6	NUM
fcis-19688	124	3	]	]	X
fcis-19688	124	4	feng	feng	PROPN
fcis-19688	124	5	,	,	PUNCT
fcis-19688	124	6	h.	h.	PROPN
fcis-19688	124	7	,	,	PUNCT
fcis-19688	124	8	zhang	zhang	PROPN
fcis-19688	124	9	,	,	PUNCT
fcis-19688	124	10	p.	p.	PROPN
fcis-19688	124	11	,	,	PUNCT
fcis-19688	124	12	xu	xu	PROPN
fcis-19688	124	13	,	,	PUNCT
fcis-19688	124	14	x.	x.	PROPN
fcis-19688	124	15	,	,	PUNCT
fcis-19688	124	16	hao	hao	PROPN
fcis-19688	124	17	,	,	PUNCT
fcis-19688	124	18	p.	p.	PROPN
fcis-19688	124	19	,	,	PUNCT
fcis-19688	124	20	&	&	CCONJ
fcis-19688	124	21	chen	chen	PROPN
fcis-19688	124	22	,	,	PUNCT
fcis-19688	124	23	w.	w.	PROPN
fcis-19688	124	24	(	(	PUNCT
fcis-19688	124	25	2019	2019	NUM
fcis-19688	124	26	)	)	PUNCT
fcis-19688	124	27	.	.	PUNCT
fcis-19688	125	1	an	an	DET
fcis-19688	125	2	unsupervised	unsupervised	ADJ
fcis-19688	125	3	suggestive	suggestive	ADJ
fcis-19688	125	4	annotation	annotation	NOUN
fcis-19688	125	5	algorithm	algorithm	NOUN
fcis-19688	125	6	for	for	ADP
fcis-19688	125	7	3d	3d	PROPN
fcis-19688	125	8	ct	ct	PROPN
fcis-19688	125	9	image	image	NOUN
fcis-19688	125	10	processing	processing	NOUN
fcis-19688	125	11	.	.	PUNCT
fcis-19688	126	1	jisuanji	jisuanji	PROPN
fcis-19688	126	2	fuzhu	fuzhu	PROPN
fcis-19688	126	3	sheji	sheji	PROPN
fcis-19688	126	4	yu	yu	PROPN
fcis-19688	126	5	tuxingxue	tuxingxue	NOUN
fcis-19688	126	6	xuebao/	xuebao/	NUM
fcis-19688	126	7	journal	journal	NOUN
fcis-19688	126	8	of	of	ADP
fcis-19688	126	9	computer	computer	NOUN
fcis-19688	126	10	-	-	PUNCT
fcis-19688	126	11	aided	aid	VERB
fcis-19688	126	12	design	design	NOUN
fcis-19688	126	13	and	and	CCONJ
fcis-19688	126	14	computer	computer	NOUN
fcis-19688	126	15	graphics	graphic	NOUN
fcis-19688	126	16	,	,	PUNCT
fcis-19688	126	17	31	31	NUM
fcis-19688	126	18	(	(	PUNCT
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fcis-19688	181	7	neural	neural	ADJ
fcis-19688	181	8	networks	network	NOUN
fcis-19688	181	9	.	.	PUNCT
fcis-19688	182	1	in	in	ADP
fcis-19688	182	2	2022	2022	NUM
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fcis-19688	182	4	16th	16th	ADJ
fcis-19688	182	5	international	international	ADJ
fcis-19688	182	6	conference	conference	NOUN
fcis-19688	182	7	on	on	ADP
fcis-19688	182	8	semantic	semantic	ADJ
fcis-19688	182	9	computing	computing	NOUN
fcis-19688	182	10	(	(	PUNCT
fcis-19688	182	11	icsc	icsc	PROPN
fcis-19688	182	12	)	)	PUNCT
fcis-19688	182	13	(	(	PUNCT
fcis-19688	182	14	pp	pp	X
fcis-19688	182	15	.	.	PUNCT
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fcis-19688	183	2	-	-	SYM
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fcis-19688	183	5	.	.	PUNCT
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fcis-19688	185	3	]	]	X
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fcis-19688	185	5	,	,	PUNCT
fcis-19688	185	6	s.	s.	PROPN
fcis-19688	185	7	(	(	PUNCT
fcis-19688	185	8	2022	2022	NUM
fcis-19688	185	9	)	)	PUNCT
fcis-19688	185	10	.	.	PUNCT
fcis-19688	186	1	image	image	NOUN
fcis-19688	186	2	processing	processing	NOUN
fcis-19688	186	3	method	method	NOUN
fcis-19688	186	4	based	base	VERB
fcis-19688	186	5	on	on	ADP
fcis-19688	186	6	fgca	fgca	NOUN
fcis-19688	186	7	and	and	CCONJ
fcis-19688	186	8	artificial	artificial	ADJ
fcis-19688	186	9	neural	neural	ADJ
fcis-19688	186	10	network	network	NOUN
fcis-19688	186	11	.	.	PUNCT
fcis-19688	187	1	scientific	scientific	ADJ
fcis-19688	187	2	programming	programming	NOUN
fcis-19688	187	3	,	,	PUNCT
fcis-19688	187	4	2022(19	2022(19	NUM
fcis-19688	187	5	)	)	PUNCT
fcis-19688	187	6	,	,	PUNCT
fcis-19688	187	7	2022	2022	NUM
fcis-19688	187	8	.	.	PUNCT
fcis-19688	188	1	[	[	X
fcis-19688	188	2	24	24	NUM
fcis-19688	188	3	]	]	X
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fcis-19688	188	5	,	,	PUNCT
fcis-19688	188	6	c.	c.	PROPN
fcis-19688	188	7	,	,	PUNCT
fcis-19688	188	8	cui	cui	PROPN
fcis-19688	188	9	,	,	PUNCT
fcis-19688	188	10	j.	j.	PROPN
fcis-19688	188	11	,	,	PUNCT
fcis-19688	188	12	shang	shang	PROPN
fcis-19688	188	13	,	,	PUNCT
fcis-19688	188	14	r.	r.	PROPN
fcis-19688	188	15	,	,	PUNCT
fcis-19688	188	16	xiao	xiao	PROPN
fcis-19688	188	17	,	,	PUNCT
fcis-19688	188	18	y.	y.	PROPN
fcis-19688	188	19	,	,	PUNCT
fcis-19688	188	20	jia	jia	PROPN
fcis-19688	188	21	,	,	PUNCT
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fcis-19688	188	23	,	,	PUNCT
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fcis-19688	188	26	,	,	PUNCT
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fcis-19688	188	29	2022	2022	NUM
fcis-19688	188	30	)	)	PUNCT
fcis-19688	188	31	.	.	PUNCT
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fcis-19688	189	2	problem	problem	NOUN
fcis-19688	189	3	detection	detection	NOUN
fcis-19688	189	4	in	in	ADP
fcis-19688	189	5	peer	peer	NOUN
fcis-19688	189	6	assessment	assessment	NOUN
fcis-19688	189	7	through	through	ADP
fcis-19688	189	8	pseudo	pseudo	NOUN
fcis-19688	189	9	-	-	NOUN
fcis-19688	189	10	labeling	labeling	NOUN
fcis-19688	189	11	using	use	VERB
fcis-19688	189	12	semi	semi	ADJ
fcis-19688	189	13	-	-	ADJ
fcis-19688	189	14	supervised	supervised	ADJ
fcis-19688	189	15	learning	learning	NOUN
fcis-19688	189	16	.	.	PUNCT
fcis-19688	190	1	international	international	ADJ
fcis-19688	190	2	educational	educational	ADJ
fcis-19688	190	3	data	datum	NOUN
fcis-19688	190	4	mining	mining	NOUN
fcis-19688	190	5	society	society	NOUN
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fcis-19688	191	3	]	]	X
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fcis-19688	191	5	,	,	PUNCT
fcis-19688	191	6	s.	s.	PROPN
fcis-19688	191	7	,	,	PUNCT
fcis-19688	191	8	zhou	zhou	PROPN
fcis-19688	191	9	,	,	PUNCT
fcis-19688	191	10	w.	w.	PROPN
fcis-19688	191	11	,	,	PUNCT
fcis-19688	191	12	qin	qin	PROPN
fcis-19688	191	13	,	,	PUNCT
fcis-19688	191	14	c.	c.	PROPN
fcis-19688	191	15	,	,	PUNCT
fcis-19688	191	16	liao	liao	PROPN
fcis-19688	191	17	,	,	PUNCT
fcis-19688	191	18	j.	j.	PROPN
fcis-19688	191	19	,	,	PUNCT
fcis-19688	191	20	huang	huang	PROPN
fcis-19688	191	21	,	,	PUNCT
fcis-19688	191	22	j.	j.	PROPN
fcis-19688	191	23	,	,	PUNCT
fcis-19688	191	24	yang	yang	PROPN
fcis-19688	191	25	,	,	PUNCT
fcis-19688	191	26	w.	w.	PROPN
fcis-19688	191	27	,	,	PUNCT
fcis-19688	191	28	&	&	CCONJ
fcis-19688	191	29	yao	yao	PROPN
fcis-19688	191	30	,	,	PUNCT
fcis-19688	191	31	j.	j.	PROPN
fcis-19688	191	32	(	(	PUNCT
fcis-19688	191	33	2023	2023	NUM
fcis-19688	191	34	,	,	PUNCT
fcis-19688	191	35	october	october	PROPN
fcis-19688	191	36	)	)	PUNCT
fcis-19688	191	37	.	.	PUNCT
fcis-19688	192	1	an	an	DET
fcis-19688	192	2	unsupervised	unsupervised	ADJ
fcis-19688	192	3	multispectral	multispectral	ADJ
fcis-19688	192	4	image	image	NOUN
fcis-19688	192	5	registration	registration	NOUN
fcis-19688	192	6	network	network	NOUN
fcis-19688	192	7	for	for	ADP
fcis-19688	192	8	skin	skin	NOUN
fcis-19688	192	9	diseases	disease	NOUN
fcis-19688	192	10	.	.	PUNCT
fcis-19688	193	1	in	in	ADP
fcis-19688	193	2	international	international	ADJ
fcis-19688	193	3	conference	conference	NOUN
fcis-19688	193	4	on	on	ADP
fcis-19688	193	5	medical	medical	ADJ
fcis-19688	193	6	image	image	NOUN
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fcis-19688	193	10	intervention	intervention	NOUN
fcis-19688	193	11	(	(	PUNCT
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fcis-19688	193	13	.	.	PUNCT
fcis-19688	193	14	720	720	NUM
fcis-19688	193	15	-	-	SYM
fcis-19688	193	16	729	729	NUM
fcis-19688	193	17	)	)	PUNCT
fcis-19688	193	18	.	.	PUNCT
fcis-19688	194	1	cham	cham	PROPN
fcis-19688	194	2	:	:	PUNCT
fcis-19688	194	3	springer	springer	NOUN
fcis-19688	194	4	nature	nature	PROPN
fcis-19688	194	5	switzerland	switzerland	PROPN
fcis-19688	194	6	.	.	PUNCT