  Whole slide image (WSI) classification often relies on deep weakly supervised
multiple instance learning (MIL) methods to handle gigapixel resolution images
and slide-level labels. Yet the decent performance of deep learning comes from
harnessing massive datasets and diverse samples, urging the need for efficient
training pipelines for scaling to large datasets and data augmentation
techniques for diversifying samples. However, current MIL-based WSI
classification pipelines are memory-expensive and computation-inefficient since
they usually assemble tens of thousands of patches as bags for computation. On
the other hand, despite their popularity in other tasks, data augmentations are
unexplored for WSI MIL frameworks. To address them, we propose ReMix, a general
and efficient framework for MIL based WSI classification. It comprises two
steps: reduce and mix. First, it reduces the number of instances in WSI bags by
substituting instances with instance prototypes, i.e., patch cluster centroids.
Then, we propose a ``Mix-the-bag'' augmentation that contains four online,
stochastic and flexible latent space augmentations. It brings diverse and
reliable class-identity-preserving semantic changes in the latent space while
enforcing semantic-perturbation invariance. We evaluate ReMix on two public
datasets with two state-of-the-art MIL methods. In our experiments, consistent
improvements in precision, accuracy, and recall have been achieved but with
orders of magnitude reduced training time and memory consumption, demonstrating
ReMix's effectiveness and efficiency. Code is available.
