CompDiff: Hierarchical Compositional Diffusion for Fair and Zero-Shot Intersectional Medical Image Generation
arXiv:2603.16551v2 Announce Type: replace-cross Abstract: Generative models are increasingly used to augment medical imaging datasets for fairer AI, yet a key assumption often goes unexamined: that generators produce equally high-quality images across demographic groups. Models trained on imbalanced data inherit these imbalances, degrading synthesis for rare subgroups and struggling with intersections absent from training: the imbalanced generator problem. Remedies such as loss reweighting operate at the optimization level and provide limited benefit when training signal is scarce or absent. W
The increasing use of generative AI in medical imaging highlights the urgent need to address biases in synthesized data that reinforce existing demographic disparities.
Ensuring fairness and zero-shot capabilities in medical image generation is critical for reliable AI diagnostics and equitable healthcare outcomes across diverse populations.
New methods are emerging to address the imbalanced generator problem, allowing for the creation of high-quality, demographically diverse medical images even for underrepresented groups.
- · AI healthcare providers
- · Underserved demographic groups
- · Medical AI researchers
- · Fair AI ethicists
- · Generative AI models with inherent data biases
- · Healthcare systems perpetuating algorithmic bias
- · AI developers ignoring fairness in medical imaging
Improved diagnostic accuracy and reduced health disparities through more robust and representative medical AI training data.
Increased trust in AI-driven medical tools, leading to wider adoption and integration into clinical practice.
The development of new regulatory frameworks specifically addressing bias and fairness in AI-generated medical data.
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Read at arXiv cs.AI