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This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

In this study, deep phenotyping of 68 patients with XPD gene defects successfully separated individuals by clinical diagnosis and survival status, potentially improving diagnosis and prognosis for xeroderma pigmentosum and trichothiodystrophy.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

This review highlights recent research using single-cell RNA sequencing and machine learning in wound healing, revealing significant insights into fibroblast diversity, immune cell dynamics, and the spatial organization of cells, which may transform therapeutic strategies for chronic wounds, fibrosis, and tissue regeneration.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.