Mass segmentation for whole mammograms via attentive multi-task learning framework

Mass segmentation in the mammogram is a necessary and challenging task in the computer-aided diagnosis of breast cancer. Most of the existing methods tend to segment the mass by manually or automatically extracting mass-centered image patches. However, manual patch extraction is time-consuming, wheras automatic patch extraction can introduce errors that will affect the performance of subsequent segmentation. In order to improve the efficiency of mass segmentation and reduce segmentation errors, we proposed a novel mass segmentation method based on an attentive multi-task learning network (MTLNet), which is an end-to-end model to accurately segment mass in the whole mammogram directly, without the need for extraction in advance with the center of mass image patch. In MTLNet, we applied group convolution to the feature extraction network, which not only reduced the redundancy of the network but also improved the capacity of feature learning. Secondly, an attention mechanism is added to the backbone to highlight the feature channels that contain rich information. Eventually, the multi-task learning framework is employed in the model, which reduces the risk of model overfitting and enables the model not only to segment the mass but also to classify and locate the mass. We used five-fold cross validation to evaluate the performance of the proposed method under detection and segmentation tasks respectively on the two public mammographic datasets INbreast and CBIS-DDSM, and our method achieved a Dice index of 0.826 on INbreast and 0.863 on CBIS-DDSM.