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Whole Slide Image (WSI) classification remains a challenge due to theirextremely 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 patchembedding module and a bag-level classification module, but they areprohibitively expensive to be trained in an end-to-end manner. Therefore,existing methods usually train them separately, or directly skip the trainingof the embedder. Such schemes hinder the patch embedder's access to slide-levelsemantic labels, resulting in inconsistency within the entire MIL pipeline. Toovercome this issue, we propose a novel framework called Iteratively CoupledMIL (ICMIL), which bridges the loss back-propagation process from the bag-levelclassifier to the patch embedder. In ICMIL, we use category information in thebag-level classifier to guide the patch-level fine-tuning of the patch featureextractor. The refined embedder then generates better instance representationsfor achieving a more accurate bag-level classifier. By coupling the patchembedder and bag classifier at a low cost, our proposed framework enablesinformation exchange between the two modules, benefiting the entire MILclassification model. We tested our framework on two datasets using threedifferent backbones, and our experimental results demonstrate consistentperformance improvements over state-of-the-art MIL methods. The code isavailable at: https://github.com/Dootmaan/ICMIL.

Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification