{"ID":6626558,"CreatedAt":"2026-07-15T02:56:36.47817413Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12987","arxiv_id":"2607.12987","title":"Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification","abstract":"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 segmentation masking, enabling scalability to datasets without pre-made lesion masks. We grow a 656-image dataset by more than 400x and validate across two datasets: biopsy-confirmed Diverse Dermatology Images (DDI) and expert-verified Fitzpatrick17k (F17k). On the DDI benchmark, we achieve malignancy classification accuracy of 86.4% under synthetic-only training and 90.9% state-of-the-art performance with real data fine-tuning, alongside leading fairness metrics. Cross-dataset experiments show +13.9% accuracy improvements on unseen F17k data despite minimal disease overlap. We openly release 266k+ synthetic images, code, and generative models to further support fairness research at https://github.com/hectorcarrion/ControllableGenDDI.","short_abstract":"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 Div...","url_abs":"https://arxiv.org/abs/2607.12987","url_pdf":"https://arxiv.org/pdf/2607.12987v1","authors":"[\"Héctor Carrión\",\"Narges Norouzi\"]","published":"2026-07-14T17:32:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":614268,"CreatedAt":"2026-07-15T02:56:36.47817413Z","UpdatedAt":"2026-07-15T02:56:36.47817413Z","DeletedAt":null,"paper_id":6626558,"paper_url":"https://arxiv.org/abs/2607.12987","paper_title":"Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification","repo_url":"https://github.com/hectorcarrion/ControllableGenDDI","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}