Generative adversarial networks and adversarial methods in biomedical image analysis
arXiv:1810.10352
Abstract
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game. Adversarial techniques have been extensively used to synthesize and analyze biomedical images. We provide an introduction to GANs and adversarial methods, with an overview of biomedical image analysis tasks that have benefited from such methods. We conclude with a discussion of strengths and limitations of adversarial methods in biomedical image analysis, and propose potential future research directions.
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- Deep Learning in Medical Ultrasound Image Segmentation: a Review
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- Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation