Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Need

Weakly supervised whole slide image classification is usually formulated as a multiple <PRE_TAG>instance learning (MIL)</POST_TAG> problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an <PRE_TAG>instance classifier</POST_TAG> through pseudo-labeling or aggregate instance features into a bag feature through attention mechanisms and then train a bag classifier, where the attention scores can be used for instance-level classification. However, the pseudo instance labels constructed by the former usually contain a lot of noise, and the attention scores constructed by the latter are not accurate enough, both of which affect their performance. In this paper, we propose an instance-level MIL framework based on contrastive learning and prototype learning to effectively accomplish both instance classification and bag classification tasks. To this end, we propose an instance-level weakly supervised contrastive learning algorithm for the first time under the MIL setting to effectively learn <PRE_TAG>instance feature representation</POST_TAG>. We also propose an accurate pseudo label generation method through prototype learning. We then develop a joint training strategy for weakly supervised contrastive learning, prototype learning, and <PRE_TAG>instance classifier</POST_TAG> training. Extensive experiments and visualizations on four datasets demonstrate the powerful performance of our method. Codes will be available.