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A synthetic image generator can not only enhance training efficiency but also provide accurate bias detection in medical classifiers, outperforming traditional methods.
A new diffusion architecture that explicitly disentangles demographic factors allows for generating higher-quality medical images for underrepresented groups and novel demographic intersections, outperforming standard fine-tuning and FairDiffusion.