# Towards disease-aware image editing of chest X-rays

arXiv: https://arxiv.org/abs/2109.01071 · HTML: https://mlsanity.com/paper/2109.01071/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Collaborative Learning for Faster StyleGAN Embedding](https://mlsanity.com/paper/2007.01758/) (2020)
- [Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space](https://mlsanity.com/paper/1907.09607/) (2019)
- [Semi-supervised Image Attribute Editing using Generative Adversarial Networks](https://mlsanity.com/paper/1907.01841/) (2019)

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

- [Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods](https://mlsanity.com/paper/2207.09740/) (2022)
- [Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation](https://mlsanity.com/paper/2203.05212/) (2022)

## Abstract (arXiv)

Disease-aware image editing by means of generative adversarial networks (GANs) constitutes a promising avenue for advancing the use of AI in the healthcare sector. Here, we present a proof of concept of this idea. While GAN-based techniques have been successful in generating and manipulating natural images, their application to the medical domain, however, is still in its infancy. Working with the CheXpert data set, we show that StyleGAN can be trained to generate realistic chest X-rays. Inspired by the Cyclic Reverse Generator (CRG) framework, we train an encoder that allows for faithfully inverting the generator on synthetic X-rays and provides organ-level reconstructions of real ones. Employing a guided manipulation of latent codes, we confer the medical condition of cardiomegaly (increased heart size) onto real X-rays from healthy patients. This work was presented in the Medical Imaging meets Neurips Workshop 2020, which was held as part of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) in Vancouver, Canada