arXiv:2610.08086v1 Announce Type: new
Abstract: The clinical integration of AI systems in digital dermatology relies heavily on human trust. Clinically interpretable visual concepts can act as interm...
By Linda Wermelinger, Simone Lionetti, Fabian Gr\"oger, Nipun Ranasekara, Philippe Gottfrois, Ludovic Amruthalingam, Labelling Consortium, Marc Pouly, Alexander A. Navarini
The study examines why dermatology AI models, largely trained on light‑skinned, cancer‑focused images, perform poorly when applied to diverse patient populations. By comparing a cancer‑trained baseline, two dermatology foundation models, and a general‑purpose vision model on tone‑stratified and disease‑shifted datasets, the authors find that disease‑distribution shift, rather than skin‑tone underrepresentation, is the primary cause of generalization failure. Representation analysis shows that cancer‑specialized features lack transferable structure, while dermatology‑pretrained features maintain stronger clustering, and lightweight adaptation with about ten labeled examples per category can recover most performance.
By Nirajan Kunwor, Sanjaya Poudel, Quoc-Huy Trinh, Jahidul Arafat, Sunil Kumar Gaire
arXiv:2609.36400v1 Announce Type: cross
Abstract: Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues su...
By Youssef Attia, Debasmita Mukherjee
The paper evaluates how well generalist and dermatology-specific machine learning models perform on diverse skin lesion datasets, including dermoscopic images and smartphone photographs. It benchmarks a range of architectures—general-purpose vision-language models, foundation models, and task-specific dermatology classifiers—under conditions of distribution shift, modality change, and demographic variability. The study quantifies the performance gap between current state‑of‑the‑art models and the robustness needed for safe, equitable clinical deployment.
By Emanoel dos Santos, Kelvin Cunha, Rodrigo Mota, Fabio Papais, Thales Bezerra, Natalia Lopes, Erico Medeiros, Shirley Cruz, Jessica Araujo, Paulo Borba, Tsang Ing Ren
CG-HAF is a global‑local fusion framework for ordinal acne severity grading that explicitly combines holistic facial severity probabilities with structured lesion‑burden descriptors such as lesion count, detection confidence, and lesion area. The model uses a lightweight, interpretable classifier to produce the final grade, achieving statistically significant improvements over global‑evidence‑only baselines, especially for severe cases. Cross‑dataset testing reveals that strong performance within a dataset does not automatically transfer, largely due to mismatched grading criteria rather than detection failures.
By Muhammad Muhtasim Shahriar, Md. Naimur Asif Borno, Saad Aloteibi, Mohammad Ali Moni
arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.
By Zehao Liu, Weijieying Ren, Jipeng Zhang, Tianxiang Zhao, Jingxi Zhu, Xiaoting Li, Vasant G Honavar