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We propose a new generative adversarial architecture to mitigate imbalancedata problem in medical image semantic segmentation where the majority ofpixels belongs to a healthy region and few belong to lesion or non-healthregion. A model trained with imbalanced data tends to bias toward healthy datawhich is not desired in clinical applications and predicted outputs by thesenetworks have high precision and low sensitivity. We propose a new conditionalgenerative refinement network with three components: a generative, adiscriminative, and a refinement network to mitigate unbalanced data problemthrough ensemble learning. The generative network learns to a segment at thepixel level by getting feedback from the discriminative network according tothe true positive and true negative maps. On the other hand, the refinementnetwork learns to predict the false positive and the false negative masksproduced by the generative network that has significant value, especially inmedical application. The final semantic segmentation masks are then composed bythe output of the three networks. The proposed architecture showsstate-of-the-art results on LiTS-2017 for liver lesion segmentation, and twomicroscopic cell segmentation datasets MDA231, PhC-HeLa. We have achievedcompetitive results on BraTS-2017 for brain tumour segmentation.

Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation