3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework
@article{Haque20213NGANSC, title={3N-GAN: Semi-Supervised Classification of X-Ray Images with a 3-Player Adversarial Framework}, author={Shafinul Haque and Ayaan Haque}, journal={ArXiv}, year={2021}, volume={abs/2109.13862} }
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…
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