3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework
This addresses the challenge of limited labeled data in medical imaging for researchers and practitioners, though it appears incremental as it builds on existing GAN and SSL methods.
The paper tackles the problem of semi-supervised classification in medical imaging where datasets lack unlabeled data, proposing 3N-GAN, a 3-player adversarial framework that integrates a classifier to improve classification performance and GAN generations over existing algorithms.
The success of deep learning for medical imaging tasks, such as classification, is heavily reliant on the availability of large-scale datasets. However, acquiring datasets with large quantities of labeled data is challenging, as labeling is expensive and time-consuming. Semi-supervised learning (SSL) is a growing alternative to fully-supervised learning, but requires unlabeled samples for training. In medical imaging, many datasets lack unlabeled data entirely, so SSL can't be conventionally utilized. We propose 3N-GAN, or 3 Network Generative Adversarial Networks, to perform semi-supervised classification of medical images in fully-supervised settings. We incorporate a classifier into the adversarial relationship such that the generator trains adversarially against both the classifier and discriminator. Our preliminary results show improved classification performance and GAN generations over various algorithms. Our work can seamlessly integrate with numerous other medical imaging model architectures and SSL methods for greater performance.