  Multiple Instance Learning (MIL), a powerful strategy for weakly supervised
learning, is able to perform various prediction tasks on gigapixel Whole Slide
Images (WSIs). However, the tens of thousands of patches in WSIs usually incur
a vast computational burden for image augmentation, limiting the MIL model's
improvement in performance. Currently, the feature augmentation-based MIL
framework is a promising solution, while existing methods such as Mixup often
produce unrealistic features. To explore a more efficient and practical
augmentation method, we introduce the Diffusion Model (DM) into MIL for the
first time and propose a feature augmentation framework called AugDiff.
Specifically, we employ the generation diversity of DM to improve the quality
of feature augmentation and the step-by-step generation property to control the
retention of semantic information. We conduct extensive experiments over three
distinct cancer datasets, two different feature extractors, and three prevalent
MIL algorithms to evaluate the performance of AugDiff. Ablation study and
visualization further verify the effectiveness. Moreover, we highlight
AugDiff's higher-quality augmented feature over image augmentation and its
superiority over self-supervised learning. The generalization over external
datasets indicates its broader applications.
