Multi-class Generative Adversarial Networks: Improving One-class Classification of Pneumonia Using Limited Labeled Data.

Abstract

From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. With the remarkable ability of GANs in learning the distribution and generating images of a particular class, they have been used for semi-supervised disease detection in medical images such as COVID-19 and Pneumonia in X-rays. However, the challenge is that if two classes of images share similar characteristics, the GAN might learn to generalize and hinder the classification of the two classes. In this paper, first we use MNIST and Fashion-MNIST datasets that are easy to visually inspect, to illustrate how similar images cause the GAN to generalize, leading to the poor classification of images. We then show how this generalization can misclassify pneumonia X-rays as healthy cases when using GANs for semi-supervised detection of pneumonia. We propose a modification to the traditional training of GANs that, using small sets of labeled data, allows for improved classification in similar classes of images in a semi-supervised learning framework.