  Whole-slide image (WSI) classification is a challenging task because 1)
patches from WSI lack annotation, and 2) WSI possesses unnecessary variability,
e.g., stain protocol. Recently, Multiple-Instance Learning (MIL) has made
significant progress, allowing for classification based on slide-level, rather
than patch-level, annotations. However, existing MIL methods ignore that all
patches from normal slides are normal. Using this free annotation, we introduce
a semi-supervision signal to de-bias the inter-slide variability and to capture
the common factors of variation within normal patches. Because our method is
orthogonal to the MIL algorithm, we evaluate our method on top of the recently
proposed MIL algorithms and also compare the performance with other
semi-supervised approaches. We evaluate our method on two public WSI datasets
including Camelyon-16 and TCGA lung cancer and demonstrate that our approach
significantly improves the predictive performance of existing MIL algorithms
and outperforms other semi-supervised algorithms. We release our code at
https://github.com/AITRICS/pathology_mil.
