  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.
