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Multiple Instance Learning (MIL) has demonstrated promise in Whole SlideImage (WSI) classification. However, a major challenge persists due to the highcomputational cost associated with processing these gigapixel images. Existingmethods generally adopt a two-stage approach, comprising a non-learnablefeature embedding stage and a classifier training stage. Though it can greatlyreduce the memory consumption by using a fixed feature embedder pre-trained onother domains, such scheme also results in a disparity between the two stages,leading to suboptimal classification accuracy. To address this issue, wepropose that a bag-level classifier can be a good instance-level teacher. Basedon this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL)to couple the embedder and the bag classifier at a low cost. ICMIL initiallyfix the patch embedder to train the bag classifier, followed by fixing the bagclassifier to fine-tune the patch embedder. The refined embedder can thengenerate better representations in return, leading to a more accurateclassifier for the next iteration. To realize more flexible and more effectiveembedder fine-tuning, we also introduce a teacher-student framework toefficiently distill the category knowledge in the bag classifier to help theinstance-level embedder fine-tuning. Thorough experiments were conducted onfour distinct datasets to validate the effectiveness of ICMIL. The experimentalresults consistently demonstrate that our method significantly improves theperformance of existing MIL backbones, achieving state-of-the-art results. Thecode is available at: https://github.com/Dootmaan/ICMIL/tree/confidence_based

Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher