Adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms

Breast cancer is currently the second most fatal cancer in women, but timely diagnosis and treatment can reduce its mortality. Breast masses are the most obvious means of cancer identification, and thus, accurate segmentation of masses is critical. In contrast to mass-centered patch segmentation, accurate segmentation of breast masses in full-field mammograms is always a challenging topic because of the extremely low signal-to-noise ratio and the uncertainty with respect to the shape, size, and location of the mass. In this study, we propose a novel adaptive channel and multiscale spatial context network for breast mass segmentation in full-field mammograms. A standard encoder-decoder structure is employed, and an elaborate adaptive channel and multiscale spatial context module (ACMSC module) is embedded in a multilevel manner in our network for accurate mass segmentation. The proposed ACMSC module utilizes the self-attention mechanism to adaptively capture discriminative contextual information among channel and spatial dimensions.The multilevel embedding of the ACMSC module enables the network to learn distinguishing features on multiple scales of feature maps. Our proposed model is evaluated on two public datasets, CBIS-DDSM and INbreast. The experimental results show that by adaptively capturing the context of the channel and spatial dimensions, our model can effectively remove false positives, predict difficult samples and achieve state-of-the-art results, with Dice coefficients of 82.81% for CBIS-DDSM and 84.11% for INbreast, respectively. We hope that our work will contribute to the CAD system for breast cancer diagnosis and ultimately improve clinical diagnosis.