Aggregated pyramid attention network for mass segmentation in mammograms

Intra-class inconsistency and inter-class indistinction are intractable problems that commonly exist in breast mass segmentation from mammograms. In this work, a novel deep learning segmentation model is presented to address these problems. Firstly, we propose a simple yet effective aggregated pyramid attention module (APAM) for capturing intra-class dependencies, aiming at effectively aggregating contextual dependencies from different receptive fields to reinforce feature representations. Then, a novel aggregated pyramid attention network (APANet) is developed for further releasing the limitation of both intra-class inconsistency and inter-class indistinction. The APANet can combine low-level spatial details and high-level contextual information via encoder-decoder structure for further refining semantic representations. Finally, our proposed APANet is greatly demonstrated on two public mammographic databases including the DDSM-BCRP and INbreast, separately achieving the Dice Similarity Coefficient (DSC) of 91.04% and 94.02%.