IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset
Researchers have developed new datasets and methods to improve AI's ability to classify skin lesions from dermatoscopic images. One paper introduces IMA++, a large dataset with over 17,000 segmentation masks from nearly 15,000 images, including multiple annotations per image to aid research on annotator preferences. Another study, DerMAE, uses synthetic data generation and knowledge distillation to train lightweight models for efficient on-device skin lesion classification, addressing class imbalance issues. A third paper proposes a contrastive meta-domain adaptation strategy to enhance the robustness of skin lesion classification models across different clinical and acquisition conditions. AI
IMPACT Advances in AI for medical imaging could lead to earlier and more accurate skin cancer detection.