• Corpus ID: 238198605

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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