Semi Supervised Medical Image Classification

Semi-supervised medical image classification aims to improve the accuracy of medical image analysis by leveraging both labeled and unlabeled data, addressing the scarcity of annotated medical images. Current research focuses on enhancing pseudo-labeling techniques through methods like class-specific distribution alignment and similarity-based approaches, often incorporating self-supervised pre-training or consistency regularization to improve the reliability of predictions from unlabeled data. These advancements are crucial for reducing the reliance on expensive and time-consuming manual annotation, ultimately improving the efficiency and accessibility of medical image diagnostics.