Multiple Instance

Multiple Instance Learning (MIL) addresses the challenge of classifying data sets where labels are assigned to groups (bags) of instances, rather than individual instances. Current research focuses on improving instance-level predictions within bags, particularly through attention mechanisms and novel pooling strategies that capture instance relationships and mitigate the effects of irrelevant instances or imbalanced data. This is being applied across diverse fields, including medical image analysis (e.g., whole slide image classification), visual grounding, and anomaly detection, where it offers a powerful approach to weakly supervised learning and improved model interpretability. The resulting advancements have significant implications for various applications by enabling efficient use of limited labeled data and enhancing the accuracy and explainability of models.