Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated images, especially for underrepresented skin tones and rare diseases, impedes progress toward measurably fair methods. We introduce cgDDI (Controllable Generation of Diverse Dermatological Imagery), a hybrid framework that (1) synthesizes realistic healthy skin samples without disturbing other input properties, (2) maps single-sample rare lesions onto novel skin-tones and locations non-parametrically, and (3) allows for efficient parametric generation with as few as 10 training samples. The framework supports both human and automated segmen...
| Authors | Héctor Carrión, Narges Norouzi |
|---|---|
| Primary category | cs.CV |
| Published | 14 July 2026 |
| Last updated at source | 15 July 2026 |
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