Rethinking Multiple Instance Learning for Whole Slide Image
  Classification: A Bag-Level Classifier is a Good Instance-Level Teacher
Hongyi Wang Luyang Luo Fang Wang Ruofeng Tong Yen-Wei Chen Hongjie Hu Lanfen Lin Hao Chen
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide
Image (WSI) classification. However, a major challenge persists due to the high
computational cost associated with processing these gigapixel images. Existing
methods generally adopt a two-stage approach, comprising a non-learnable
feature embedding stage and a classifier training stage. Though it can greatly
reduce the memory consumption by using a fixed feature embedder pre-trained on
other domains, such scheme also results in a disparity between the two stages,
leading to suboptimal classification accuracy. To address this issue, we
propose that a bag-level classifier can be a good instance-level teacher. Based
on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL)
to couple the embedder and the bag classifier at a low cost. ICMIL initially
fix the patch embedder to train the bag classifier, followed by fixing the bag
classifier to fine-tune the patch embedder. The refined embedder can then
generate better representations in return, leading to a more accurate
classifier for the next iteration. To realize more flexible and more effective
embedder fine-tuning, we also introduce a teacher-student framework to
efficiently distill the category knowledge in the bag classifier to help the
instance-level embedder fine-tuning. Thorough experiments were conducted on
four distinct datasets to validate the effectiveness of ICMIL. The experimental
results consistently demonstrate that our method significantly improves the
performance of existing MIL backbones, achieving state-of-the-art results. The
code is available at: https://github.com/Dootmaan/ICMIL/tree/confidence_based