Towards Annotation-Efficient Learning for Real-World Medical Image Analysis

Deep learning needs high-quality, ample data for accurate models. In medicine, annotations are costly. This thesis targets real-world medical image analysis with limited data. It addresses imbalanced, unlabelled, and noisy data. Proposed methods expose ideal-model flaws, enhance data use, and show universal applicability, advancing medical AI.