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Mammography is one of the most commonly applied tools for early breast cancerscreening. Automatic segmentation of breast masses in mammograms is essentialbut challenging due to the low signal-to-noise ratio and the wide variety ofmass shapes and sizes. Existing methods deal with these challenges mainly byextracting mass-centered image patches manually or automatically. However,manual patch extraction is time-consuming and automatic patch extraction bringserrors that could not be compensated in the following segmentation step. Inthis study, we propose a novel attention-guided dense-upsampling network(AUNet) for accurate breast mass segmentation in whole mammograms directly. InAUNet, we employ an asymmetrical encoder-decoder structure and propose aneffective upsampling block, attention-guided dense-upsampling block (AU block).Especially, the AU block is designed to have three merits. Firstly, itcompensates the information loss of bilinear upsampling by dense upsampling.Secondly, it designs a more effective method to fuse high- and low-levelfeatures. Thirdly, it includes a channel-attention function to highlightrich-information channels. We evaluated the proposed method on two publiclyavailable datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-artfully convolutional networks, AUNet achieved the best performances with anaverage Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% forINbreast.

AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms