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Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

7 Oct 2020arXiv:2010.03975archive 2025-07-28

Bradley Segal, David M. Rubin, Grace Rubin, Adam Pantanowitz

Chest x-rays are a vital tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled data to develop new diagnostic tools, however this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Previous work has sought to address these concerns by creating class-specific GANs that synthesise images to augment training data. These approaches cannot be scaled as they introduce computational trade offs between model size and class number which places fixed limits on the quality that such generates can achieve. We address this concern by introducing latent class optimisation which enables efficient, multi-modal sampling from a GAN and with which we synthesise a large archive of labelled generates. We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples. We provide an in depth review of the properties of varying pathologies seen on generates as well as an overview of the extent of disease diversity captured by the model. We validate the application of the Fr\'echet Inception Distance (FID) to measure the quality of x-ray generates and find that they are similar to other high resolution tasks. We quantify x-ray clinical realism by asking radiologists to distinguish between real and fake scans and find that generates are more likely to be classed as real than by chance, but there is still progress required to achieve true realism. We confirm these findings by evaluating synthetic classification model performance on real scans. We conclude by discussing the limitations of PGGAN generates and how to achieve controllable, realistic generates.

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BradSegal/CXR_PGGAN officialpytorch report

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Tasks

Conditional Image Generation Data Augmentation Diagnostic Ethics Image Generation Medical Image Generation Synthetic Data Generation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Medical Image Generation	ChestXray14 1024x1024	Progressive Growing GAN	FID	8.02	#2 of 2	Archive leaderboard	report

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Methods

1x1 Convolution Average Pooling Batch Normalization Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Latent Optimisation Local Response Normalization Max Pooling ProGAN ReLU WGAN-GP Loss

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