arXiv Computer Vision
Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology
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arXiv Computer Vision
3d ago
When Annotation Patterns Are Learned: Skin-Tone Dependence in Dermatology Concepts
Concept bottleneck models (CBMs) rely on reliable concept annotations to allow clinicians to inspect and correct skin disease diagnoses. An audit of the SkinCon dermatology concept dataset reveals a tone‑dependent pattern: erythema is annotated far less often on dark skin (odds ratio 0.17) while hyperpigmentation and hypopigmentation are annotated more often (odds ratios 6.2 and 4.5). An evidential concept model trained on these annotations reproduces the same bias, and three label‑error detectors perform worse than a disease‑only baseline at detecting erythema mentioned by dermatologists but absent in SkinCon labels. The study concludes that concept annotations must be audited within each subgroup before they can be trusted for explanations.
arXiv Computer Vision
Oct 5
Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification
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.
arXiv AI
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Disease Burden over Skin Tone: Decomposing the Dermatology-AI Generalization Gap
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.
arXiv Machine Learning
Sep 30
CAMEO: A Class-Activation-Mapped Equitable Overlay Framework for Fair and Robust Deep Learning-based Skin Condition Diagnosis
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...
arXiv Computer Vision
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Benchmarking Off-the-Shelf Multimodal AI Models Against Dermatologists on Patient-Captured Skin Images
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arXiv AI
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Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
arXiv:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.