Abstract. We explore the use of deep learning for breast mass segmentation in mammograms. By integrating the merits of residual learning and probabilistic graphical modelling with standard U-Net, we propose a new deep network, Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation performance. Benefiting from the advantage of probabilistic graphical modelling in the pixel-level labelling, and the structure insights of a deep residual network in the feature extraction, the CRU-Net provides excellent mass segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets demonstrate that the CRU-Net achieves the best mass segmentation performance compared to the state-of-art methodologies. Moreover, neither tedious pre-processing nor post-processing techniques are not required in our algorithm.
Li et al. REF integrate benefits of residual learning to improve the performance of standard U-Net segmentation to address gradient vanishing and exploding problems with increasing CNN depth.
Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net
ArXiv
ArXiv
Computer Science