Background/Objectives: Dermoscopic skin lesion classifiers often lose accuracy under domain shift across imaging devices, illumination, and capture artifacts. We study how data augmentation improves the robustness of a binary malignant-versus-non-malignant classifier, with emphasis on out-of-domain (OOD) generalization.
arXiv:2607. 26765v1 Announce Type: cross Abstract: Background/Objectives: Dermoscopic skin lesion classifiers often lose accuracy under domain shift across imaging devices, illumination, and capture artifacts.
arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.
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...
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.
The paper introduces AdaConRed, a label‑free post‑conformal decision rule that transforms ambiguous conformal prediction sets into single class assignments for medical image classification. It employs a five‑stage pipeline—vision‑language generative augmentation, a frozen DermFoundation encoder, a lightweight MLP classifier, an entropy‑modulated margin‑aware nonconformity score, and a reassignment step for transitional samples—to improve accuracy on OSCC and ISIC benchmarks. Results show notable gains in malignant class accuracy while maintaining overall performance.
LENS‑GRF is a permutation‑invariant lesion evidence network that uses a Set‑Transformer and gated residual fusion to combine global facial context with localized lesion patches for four‑class acne severity grading. The framework integrates adaptive facial skin segmentation, a Vision Transformer prior, and a lesion set transformer that encodes spatial geometry, with a gating mechanism that modulates local residual contributions. In experiments on ACNE04 and PLSBRACNE01, the fully automated model achieved 80.82% accuracy, while using ground‑truth lesion annotations raised accuracy to 95.89% and a Quadratic Weighted Kappa of 0.9753; zero‑shot evaluation on the full cohort yielded 35.00% accuracy versus 42.50% for a global baseline, and oracle analyses on a 148‑subject cohort showed improved accuracy and QWK up to 47.97% and 0.5799.
arXiv:2609.07180v1 Announce Type: cross Abstract: Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical managem...
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.
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...
FedHisto-PAST v2 is a parameter‑efficient, stain‑aware federated learning framework for cross‑site lung histopathology classification, combining a frozen HIBOU‑B foundation model with techniques such as paired‑view prediction, feature consistency, prototype learning, and adaptive aggregation. In a five‑client, non‑IID simulation and an exploratory LungHist700 cohort, the method achieved a Macro‑F1 of 0.7286 and a balanced accuracy of 0.7305, with the prediction‑level consistency component providing the most clear independent benefit. The framework updated only about 1.25% of the model parameters, demonstrating efficient adaptation while acknowledging limitations in privacy guarantees and clinical validation.
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.
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.