Whole Slide Image (WSI) classification remains a challenge due to their
extremely high resolution and the absence of fine-grained labels. Presently,
WSIs are usually classified as a Multiple Instance Learning (MIL) problem when
only slide-level labels are available. MIL methods involve a patch embedding
process and a bag-level classification process, but they are prohibitively
expensive to be trained end-to-end. 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 labels, resulting in
inconsistencies 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 processes, 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. Code will be made available upon acceptance