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Abstract #1718

Automatic breast lesion segmentation in MR images employing a dense attention fully convolutional network

Cheng Li1, Hui Sun2, Qiegen Liu3, Zaiyi Liu4, Meiyun Wang5, Hairong Zheng1, and Shanshan Wang1

1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2School of Control Science and Engineering, Shandong University, Shangdong, China, 3Department of Electronic Information Engineering, Nanchang University, Nanchang, China, 4Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, 5Henan Provincial Peoples Hospital, Henan, China

Despite its high sensitivity, MR imaging has low specificity and high false positive issues. Therefore, automatic breast lesion detection algorithms are necessary. To this end, we propose a new network, dense attention network (DANet), for breast lesion segmentation in MR images. In DANet, we designed a feature fusion and selection mechanism. Features from the corresponding encoder layer and from all previous decoder layers are fused by concatenation. To highlight the rich-informative channels, a channel attention module is introduced. DANet showed better segmentation results compared to commonly applied segmentation networks on our 2D contrast-enhanced T1-weighted breast MR dataset.

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