# Generative adversarial networks and adversarial methods in biomedical image analysis

arXiv: https://arxiv.org/abs/1810.10352 · HTML: https://mlsanity.com/paper/1810.10352/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Data Augmentation For Medical MR Image Using Generative Adversarial Networks](https://mlsanity.com/paper/2111.14297/) (2021)

## Abstract (arXiv)

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.