Rethinking Multiple Instance Learning for Whole Slide Image
  Classification: A Good Instance Classifier is All You Need
Linhao Qu Yingfan Ma Xiaoyuan Luo Manning Wang Zhijian Song
Weakly supervised whole slide image classification is usually formulated as a
multiple instance learning (MIL) 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 instance classifier 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 instance feature representation. 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 instance classifier training. Extensive experiments and
visualizations on four datasets demonstrate the powerful performance of our
method. Codes will be available.