Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification

Understand

The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem.

• Since the positive tissue is only a small fraction of the gigapixel WSI, existing MIL methods intuitively focus on identifying salient instances via attention mechanisms.
• However, this leads to a bias towards easy-to-classify instances while neglecting hard-to-classify instances.
• Some literature has revealed that hard examples are beneficial for modeling a discriminative boundary accurately.