# Medical Image Generation using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2005.10687 · HTML: https://mlsanity.com/paper/2005.10687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 4 of 4)

- [Black-box Membership Inference Attacks against Fine-tuned Diffusion Models](https://mlsanity.com/paper/2312.08207/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [TimEHR: Image-based Time Series Generation for Electronic Health Records](https://mlsanity.com/paper/2402.06318/) (2024)
- [Rapid Review of Generative AI in Smart Medical Applications](https://mlsanity.com/paper/2406.06627/) (2024)

## Abstract (arXiv)

Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data. The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical image generation, image reconstruction, and image-to-image translation. These properties bring the attention of the researcher in the field of medical image analysis and we are witness of rapid adaption in many novel and traditional applications. This chapter provides state-of-the-art progress in GANs-based clinical application in medical image generation, and cross-modality synthesis. The various framework of GANs which gained popularity in the interpretation of medical images, such as Deep Convolutional GAN (DCGAN), Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image translation model (UNIT), continue to improve their performance by incorporating additional hybrid architecture, has been discussed. Further, some of the recent applications of these frameworks for image reconstruction, and synthesis, and future research directions in the area have been covered.