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This paper proposes a novel approach based on conditional GenerativeAdversarial Networks (cGAN) for breast mass segmentation in mammography. Wehypothesized that the cGAN structure is well-suited to accurately outline themass area, especially when the training data is limited. The generative networklearns intrinsic features of tumors while the adversarial network enforcessegmentations to be similar to the ground truth. Experiments performed ondozens of malignant tumors extracted from the public DDSM dataset and from ourin-house private dataset confirm our hypothesis with very high Dice coefficientand Jaccard index (>94% and >89%, respectively) outperforming the scoresobtained by other state-of-the-art approaches. Furthermore, in order to detectportray significant morphological features of the segmented tumor, a specificConvolutional Neural Network (CNN) have also been designed for classifying thesegmented tumor areas into four types (irregular, lobular, oval and round),which provides an overall accuracy about 72% with the DDSM dataset.

Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification