Multiple Instance Learning Framework with Masked Hard Instance Mining
  for Gigapixel Histopathology Image Analysis
Authors: name: Wenhao Tang, name: Sheng Huang, name: Heng Fang, name: Fengtao Zhou, name: Bo Liu, name: Qingshan Liu

Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has
opened new avenues for Computational Pathology (CPath). As positive tissue
comprises only a small fraction of gigapixel WSIs, existing Multiple Instance
Learning (MIL) methods typically focus on identifying salient instances via
attention mechanisms. However, this leads to a bias towards easy-to-classify
instances while neglecting challenging ones. Recent studies have shown that
hard examples are crucial for accurately modeling discriminative boundaries.
Applying such an idea at the instance level, we elaborate a novel MIL framework
with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure
with a consistency constraint to explore the hard instances. Using a
class-aware instance probability, MHIM-MIL employs a momentum teacher to mask
salient instances and implicitly mine hard instances for training the student
model. To obtain diverse, non-redundant hard instances, we adopt large-scale
random masking while utilizing a global recycle network to mitigate the risk of
losing key features. Furthermore, the student updates the teacher using an
exponential moving average, which identifies new hard instances for subsequent
training iterations and stabilizes optimization. Experimental results on cancer
diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate
that MHIM-MIL outperforms the latest methods in both performance and
efficiency. The code is available at: https://github.com/DearCaat/MHIM-MIL.