Multiple instance learning for medical image classification based on instance importance

Multiple Instance Learning (MIL) is designed to classify instances where class labels are associated with sets of instances, a common occurrence in biomedical data, especially when multiple images are derived from a single object measurement. Among the various MIL techniques, instance-based classification emerges as the main approach. Individual instances are assigned class labels, and the final bag-level classification is determined through an aggregation process, usually based on average or maximum values. While instance-based models may exhibit slightly lower performance compared to more general methods, they offer the unique advantage of interpretability, allowing the decision-making process to be traced to each individual instance. This article presents an innovative instance-based approach that not only assigns class labels to instances, but also gives them meaning. The final aggregation uses a weighted average, allowing the model to identify instances carrying valuable information while distinguishing those that are redundant in the classification process. This enhancement gives the model the ability to recognize the meaning of individual instances, thereby increasing both its overall interpretability and classification performance.