  Multiple Instance Learning (MIL) is widely used in analyzing
histopathological Whole Slide Images (WSIs). However, existing MIL methods do
not explicitly model the data distribution, and instead they only learn a
bag-level or instance-level decision boundary discriminatively by training a
classifier. In this paper, we propose DGMIL: a feature distribution guided deep
MIL framework for WSI classification and positive patch localization. Instead
of designing complex discriminative network architectures, we reveal that the
inherent feature distribution of histopathological image data can serve as a
very effective guide for instance classification. We propose a
cluster-conditioned feature distribution modeling method and a pseudo
label-based iterative feature space refinement strategy so that in the final
feature space the positive and negative instances can be easily separated.
Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show
that our method achieves new SOTA for both global classification and positive
patch localization tasks.
