Multiple Instance Learning

Multiple Instance Learning (MIL) is a machine learning approach where the model learns from groups of data, called "bags," each containing many individual items, or "instances." Instead of knowing the exact label of each item, only the label of the entire bag is provided. The goal is to identify which instances within the bags are responsible for the bag's label. This approach is useful in situations like medical imaging, where a scan (bag) contains many regions (instances), and only the overall diagnosis is known, helping the model focus on relevant parts without needing detailed annotations.