# Synthetic Medical Imaging Generation with Generative Adversarial Networks For Plain Radiographs

arXiv: https://arxiv.org/abs/2403.19107 · HTML: https://mlsanity.com/paper/2403.19107/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Pros and Cons of GAN Evaluation Measures: New Developments](https://mlsanity.com/paper/2103.09396/) (2021)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Anycost GANs for Interactive Image Synthesis and Editing](https://mlsanity.com/paper/2103.03243/) (2021)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning](https://mlsanity.com/paper/1901.03597/) (2019)
- [IMAGINE: Image Synthesis by Image-Guided Model Inversion](https://mlsanity.com/paper/2104.05895/) (2021)
- [MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs](https://mlsanity.com/paper/1712.06957/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Generalist Model for Diverse Text-Guided Medical Image Synthesis](https://mlsanity.com/paper/2405.09806/) (2024)
- [Rapid Review of Generative AI in Smart Medical Applications](https://mlsanity.com/paper/2406.06627/) (2024)
- [Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images](https://mlsanity.com/paper/2504.15007/) (2025)

## Abstract (arXiv)

In medical imaging, access to data is commonly limited due to patient privacy restrictions and the issue that it can be difficult to acquire enough data in the case of rare diseases.[1] The purpose of this investigation was to develop a reusable open-source synthetic image generation pipeline, the GAN Image Synthesis Tool (GIST), that is easy to use as well as easy to deploy. The pipeline helps to improve and standardize AI algorithms in the digital health space by generating high quality synthetic image data that is not linked to specific patients. Its image generation capabilities include the ability to generate imaging of pathologies or injuries with low incidence rates. This improvement of digital health AI algorithms could improve diagnostic accuracy, aid in patient care, decrease medicolegal claims, and ultimately decrease the overall cost of healthcare. The pipeline builds on existing Generative Adversarial Networks (GANs) algorithms, and preprocessing and evaluation steps were included for completeness. For this work, we focused on ensuring the pipeline supports radiography, with a focus on synthetic knee and elbow x-ray images. In designing the pipeline, we evaluated the performance of current GAN architectures, studying the performance on available x-ray data. We show that the pipeline is capable of generating high quality and clinically relevant images based on a lay person's evaluation and the Fr'echet Inception Distance (FID) metric.