ProtoTree-MIL: Interpretable Multiple Instance Learning for Whole Slide Image Classification

Whole slide image (WSI) classification is one of the important fields of digital pathology, and is generally solved as a weakly supervised learning problem by adopting multiple instance learning (MIL). However, a common but crucial challenge faced by existing MIL models is their inability to provide convincing explanations that can win the trust of pathologists and be applied to clinical diagnosis. In addition, most attention-based MIL models use attention scores to represent the importance of each patch in the WSI rather than inferring patch probabilities directly, which does not accurately detect the critical patches. To address these two challenges, we propose a ProtoTree based MIL model for WSI classification, called ProtoTree-MIL, where ProtoTree is an interpretable model that combines the advantages of prototype-learning and decision tree. ProtoTree-MIL not only explains why some patches are important for the final prediction through prototype-learning, but also provides global and local explanation through decision tree. We also propose a method to infer patch probabilities and measure their importance under the framework of ProtoTree-MIL. By conducting various experiments on three public WSI datasets, Camelyon16, TCGA-NSCLC, and TCGA-RCC, we demonstrate that our proposed ProtoTree-MIL can achieve a competitive performance to the state-of-the-art MIL models but provide more persuasive explanations than them. Explicitly generating patch probabilities also makes ProtoTree-MIL more accurate to detect the key patches than other attention-based MIL models. Specially, by evaluating our model on a real clinical gastritis and gastric cancer dataset, we show the explanations provided by ProtoTree-MIL are significant and faithful.