AUNet: Breast Mass Segmentation of Whole Mammograms
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Deep learning based segmentation has seen rapid development lately in both natural and medical image processing. However, its application to mammographic mass segmentation is still a challenging task due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. In this study, we propose a new network, AUNet, for the breast mass segmentation. Different from most methods that need to extract mass-centered image patches, AUNet could directly process the whole mammograms. Furthermore, it introduces an asymmetrical structure to the traditional encoder-decoder segmentation architecture and proposes a new upsampling block, Attention Up (AU) Block. Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three existing fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8 CBIS-DDSM and 79.1
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