Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data

Understand

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.