Medical image classification based on a generative adversarial network trained discriminator

Abstract

Mechanisms are provided to implement a generative adversarial network (GAN). A discriminator of the GAN is configured to discriminate input medical images into a plurality of classes including a first class indicating a medical image representing a normal medical condition, a second class indicating an abnormal medical condition, and a third class indicating a generated medical image. A generator of the GAN generates medical images and a training medical image set is input to the discriminator that includes labeled medical images, unlabeled medical images, and generated medical images. The discriminator is trained to classify training medical images in the training medical image set into corresponding ones of the first, second, and third classes. The trained discriminator is applied to a new medical image to classify the new medical image into a corresponding one of the first class or second class. The new medical image is either labeled or unlabeled.