  We propose a new generative adversarial architecture to mitigate imbalance
data problem for the task of medical image semantic segmentation where the
majority of pixels belong to a healthy region and few belong to lesion or
non-health region. A model trained with imbalanced data tends to bias towards
healthy data which is not desired in clinical applications. We design a new
conditional GAN with two components: a generative model and a discriminative
model to mitigate imbalanced data problem through selective weighted loss.
While the generator is trained on sequential magnetic resonance images (MRI) to
learn semantic segmentation and disease classification, the discriminator
classifies whether a generated output is real or fake. The proposed
architecture achieved state-of-the-art results on ACDC-2017 for cardiac
segmentation and diseases classification. We have achieved competitive results
on BraTS-2017 for brain tumor segmentation and brain diseases classification.
