Self-Supervised Learning for Early Disease Detection in Low-Label Medical Image Datasets
Getting reliable labeled data for medical image analysis is genuinely difficult. Radiologists, pathologists, and other specialist clinicians are expensive, slow, and not universally available — particularly in low-resource clinical environments across developing nations.
self-supervised learning, medical image analysis, contrastive learning, masked autoencoders, low-label learning, disease detection, ViT, MedSSL