Multiple Instance Learning

Multiple Instance Learning (MIL) is a machine learning approach addressing the challenge of classifying data sets where labels are assigned to groups (bags) of instances, rather than individual instances. Current research heavily focuses on applying MIL to whole slide image (WSI) analysis in digital pathology, employing architectures like transformers and incorporating techniques such as attention mechanisms, pseudo-labeling, and graph neural networks to improve classification accuracy and interpretability. This work is significant because it allows for efficient analysis of large, complex medical images without requiring exhaustive pixel-level annotation, potentially leading to faster and more accurate diagnoses in healthcare.