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 study compares five pre‑trained convolutional neural networks—ResNet50, VGG16, VGG19, MobileNet, and InceptionV3—for melanoma detection using dermatoscopic and histopathological image datasets. Accuracy varied across models and modalities, with ResNet50 achieving the highest scores (84% on HAM10000 and 83% on CR‑AI4SkIN) and InceptionV3 the lowest (71% on ISIC 2018). The results show that a model’s performance on dermatoscopic images does not necessarily predict its performance on histopathological images.
By Wagner Moreno Schmitz, Marco Antonio de Castro Barbosa, Thiago Magalh\~aes Amaral, Dalcimar Casanova, Jefferson Tales Oliva
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
arXiv:2609.38560v1 Announce Type: new
Abstract: Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign in...
By Mohamed Hazem, Tarek Waleed, Omar Khaled, Nada Omar, Mahmoud Raslan, Marwa Mohamed Fawzy, Aya Fahim, Rania M. Mogawer, Ahmed Mourad, Kariman Mansour, Muhammad Rushdi
arXiv:2608. 11280v1 Announce Type: cross Abstract: Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models.
By Rofiqul Islam, Lilatul Ferdouse
Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification.
arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.
By Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev, Oleg I. Samovarov
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
The paper introduces scalable Graph Transformers for classifying healthy versus tumor epithelial cells in whole-slide images of cutaneous squamous cell carcinoma. By constructing a full‑WSI cell graph and incorporating morphological, texture, and neighboring cell class features, the proposed SGFormer and DIFFormer models outperform traditional image‑based methods, achieving balanced accuracies above 85% on single‑WSI tests and 83.6% on multi‑WSI evaluations. The study demonstrates that preserving tissue‑level context through graph representations improves classification of morphologically similar cell types.
By Lucas Sanc\'er\'e, No\'emie Moreau, Katarzyna Bozek
MDSkin-Net is a multi‑task skin lesion analysis framework that integrates Pattern Analysis priors into a hybrid CNN‑Transformer architecture. It introduces a Pattern Analysis‑Guided Attention Module (PAGAM) with improved Efficient Channel Attention, Multi‑Scale Spatial Attention, and Biased Asymmetry Attention, along with a multi‑scale spatial alignment regularization that uses segmentation masks as soft supervision. Trained only on the ISIC 2017 training split, the model achieves high segmentation and classification performance on multiple datasets, demonstrating strong zero‑shot generalization across different cohorts.
By Yijian Li, Saad Bedros, Paul Bigliardi, Mei Bigliardi Qi, Vassilios Morellas, Nikolaos Papanikolopoulos
The paper introduces a unified system that performs hair segmentation and skin characterization on macroscopic dermatological images, specifically tailored for real clinical laser treatments. It combines a custom YOLOv9 variant for instance segmentation with classical image processing and a spectrophotometric color calibration stage to extract objective parameters such as phototype, hair density, color, and thickness. Experimental results on a custom dataset show competitive segmentation performance (mAP@50 ≈ 0.80) for short, shaved hairs typical of pre‑treatment scenarios, demonstrating the pipeline’s feasibility for future automated laser parameterization.
By Alex Navarro Navarro, Marc Arriaga Hidalgo, Carlos Moreno Rubio, Maxime Talarmain, Noelia Vaquero Gallardo