Skin Lesion Classification
Overview
Skin lesion classification uses dermoscopic or clinical images, sometimes with patient metadata, to identify lesion types and estimate whether a lesion is malignant. Common approaches include transfer-learned CNNs and vision transformers, ensembles, hierarchical or graph-based models, and fusion of image features with clinical information. Researchers also explore synthetic-image augmentation, domain adaptation, fairness-aware training, and uncertainty estimates to address scarce or imbalanced data and make predictions more interpretable. High scores on benchmarks such as HAM10000 and ISIC do not ensure reliable clinical performance: external validation, calibration, image quality, and shifts in disease mix or skin tone remain important challenges.
Latest papers
58 papers6 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.