Multiple Instance Learning Framework with Masked Hard Instance Mining
  for Gigapixel Histopathology Image Analysis
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.
68ca148b6e0073c09bd1de00
Wenhao Tang
claimed_verified
2025-09-17T12:57:05.211Z
649010619852e20da842e84c
/avatars/5016c566d804d1a00fccbba69059e2ef.svg
Wenhao
user
Dearcat
68ca148b6e0073c09bd1de01
Sheng Huang
68ca148b6e0073c09bd1de02
Heng Fang
68ca148b6e0073c09bd1de03
Fengtao Zhou
68ca148b6e0073c09bd1de04
Bo Liu
68ca148b6e0073c09bd1de05
Qingshan Liu
Multiple Instance Learning Framework with Masked Hard Instance Mining
  for Gigapixel Histopathology Image Analysis
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.
A novel MIL framework, MHIM-MIL, uses masked hard instance mining with a Siamese structure and momentum teacher to improve cancer diagnosis and subtyping accuracy.
Multiple Instance Learning MIL masked hard instance mining MHIM-MIL Siamese structure consistency constraint class-aware instance probability momentum teacher large-scale random masking global recycle network exponential moving average