Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms
Jing Yan, Pierre-Henri Conze, Étienne Decencière, Mathieu Lamard, Gwenolé Quellec, Béatrice Cochener, Gouenou Coatrieux · 2019
This paper addresses breast mass segmentation from high-resolution mammograms. To cope with strong class imbalance, huge diversity of size, shape, texture and contour as well as limited receptive field, mass segmentation is achieved through a multi-scale cascade of deep convolutional encoder-decoders without any pre-detection scheme. Multi-scale information is integrated using auto-context to make long-range spatial context arising from lower scale impact training at higher resolution. The pipeline is trained end-to-end to benefit from simultaneous segmentation refinement performed at each level. It incorporates transfer learning and fine tuning from DDSM-CBIS to INbreast datasets to further improve mass delineations. The comprehensive evaluation provided for high-resolution INbreast images highlights promising model generalizability against standard encoder-decoder strategies.