  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. By applying such an idea at the
instance level, we elaborate a novel MIL framework with masked hard instance
mining (MHIM-MIL), which uses a Siamese structure (Teacher-Student) with a
consistency constraint to explore the potential hard instances. With several
instance masking strategies based on attention scores, MHIM-MIL employs a
momentum teacher to implicitly mine hard instances for training the student
model, which can be any attention-based MIL model. This counter-intuitive
strategy essentially enables the student to learn a better discriminating
boundary. Moreover, the student is used to update the teacher with an
exponential moving average (EMA), which in turn identifies new hard instances
for subsequent training iterations and stabilizes the optimization.
Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets
demonstrate that MHIM-MIL outperforms other latest methods in terms of
performance and training cost. The code is available at:
https://github.com/DearCaat/MHIM-MIL.
