  Whole Slide Image (WSI) classification remains a challenge due to their
extremely high resolution and the absence of fine-grained labels. Presently,
WSI classification is usually regarded as a Multiple Instance Learning (MIL)
problem when only slide-level labels are available. MIL methods involve a patch
embedding module and a bag-level classification module, but they are
prohibitively expensive to be trained in an end-to-end manner. Therefore,
existing methods usually train them separately, or directly skip the training
of the embedder. Such schemes hinder the patch embedder's access to slide-level
semantic labels, resulting in inconsistency within the entire MIL pipeline. To
overcome this issue, we propose a novel framework called Iteratively Coupled
MIL (ICMIL), which bridges the loss back-propagation process from the bag-level
classifier to the patch embedder. In ICMIL, we use category information in the
bag-level classifier to guide the patch-level fine-tuning of the patch feature
extractor. The refined embedder then generates better instance representations
for achieving a more accurate bag-level classifier. By coupling the patch
embedder and bag classifier at a low cost, our proposed framework enables
information exchange between the two modules, benefiting the entire MIL
classification model. We tested our framework on two datasets using three
different backbones, and our experimental results demonstrate consistent
performance improvements over state-of-the-art MIL methods. The code is
available at: https://github.com/Dootmaan/ICMIL.
