Visualizing chest X-ray dataset biases using GANs
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Abstract (arXiv)
Recent work demonstrates that images from various chest X-ray datasets contain visual features that are strongly correlated with protected demographic attributes like race and gender. This finding raises issues of fairness, since some of these factors may be used by downstream algorithms for clinical predictions. In this work, we propose a framework, using generative adversarial networks (GANs), to visualize what features are most different between X-rays belonging to two demographic subgroups.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 12 references that are in the map. First posted on arXiv 29 Apr 2023.
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10 of 12 references.
References · 10
- 2022Generative Adversarial Networkscited by 5,627
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
- 2020Training Generative Adversarial Networks with Limited Datacited by 1,186
- 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 156
- 2022Deep Learning Discovery of Demographic Biomarkers in Echocardiographycited by 12
- 2022medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Spacecited by 10
- 2022RareGAN: Generating Samples for Rare Classescited by 9
- 2019Age prediction using a large chest X-ray datasetcited by 5
- 2021Controlled AutoEncoders to Generate Faces from Voicescited by 3
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