Synthetic Leprosy Image Generation Using Mask-Conditioned Latent Diffusion and Transfer Learning from Large Chronic Wound Datasets
Bridging the diagnostic data gap through generative modeling and transfer learning for neglected tropical diseases.
Machine learning for neglected tropical diseases is limited by data, not algorithms: public annotated image sets for leprosy (Hansen's disease) number in the hundreds, orders of magnitude below what generative models require. We ask whether a model trained on abundant chronic wou...
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This research addresses the critical data scarcity gap in medical AI by leveraging synthetic data generation to overcome the limitations of rare disease diagnostics. By transferring knowledge from chronic wound datasets to leprosy, it establishes a scalable framework for training robust models on diseases lacking sufficient public documentation.
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