id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
american_scientific_journal-6070	Chun-ming , TANG ; Dong , LIU ; Xiang , YU 	MRI Image Segmentation System of Uterine Fibroids Based on AR-Unet Network	2020	10	.pdf	application/pdf	3327	178	51	In terms of deep neural networks, Kurata Y. and his colleagues [8] proposed the use of improved U-net for segmentation of uterine fibroids MRI images, that is, replacing the ReLU of each layer in the original U-net network with leaky-ReLu, And increase the dropout layer, using a batch size of 15, down sampling using 8 layers, and finally measured the average Dice coefficient of all uterine fibroids is 0.85. In order to improve the segmentation quality, we propose an AR- Unet (Attention Resnet101-Unet) network to segment uterine fibroids MRI images.	cache/american_scientific_journal-6070.pdf	txt/american_scientific_journal-6070.txt