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:2609.24190v1 Announce Type: new Abstract: Artificial intelligence (AI) has advanced at a rapid pace in recent years. Initially, breakthroughs in large language models caught widespread attentio...
The paper introduces MIFR, a modality‑invariant and fair representation framework for skin disease classification that jointly processes clinical photographs and dermoscopic images using ViT‑based encoders. It employs a five‑component multi‑objective loss to balance classification accuracy, fairness across skin tones, class alignment, and modality invariance. Experiments on paired and external datasets demonstrate competitive predictive performance and fairness, with t‑SNE visualizations confirming alignment of embeddings from different modalities.
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: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.
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.
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.
Skin diseases represent a major global public health burden, yet machine learning tools developed to assist in their diagnosis suffer from two critical limitations: reliance on only one modality for d...
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:2511. 14900v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis.
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...
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...
arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.