Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
Journal:
arXiv
Published Date:
May 2, 2025
Abstract
Multiple Instance Learning (MIL) has emerged as the best solution for Whole
Slide Image (WSI) classification. It consists of dividing each slide into
patches, which are treated as a bag of instances labeled with a global label.
MIL includes two main approaches: instance-based and embedding-based. In the
former, each patch is classified independently, and then the patch scores are
aggregated to predict the bag label. In the latter, bag classification is
performed after aggregating patch embeddings. Even if instance-based methods
are naturally more interpretable, embedding-based MILs have usually been
preferred in the past due to their robustness to poor feature extractors.
However, recently, the quality of feature embeddings has drastically increased
using self-supervised learning (SSL). Nevertheless, many authors continue to
endorse the superiority of embedding-based MIL. To investigate this further, we
conduct 710 experiments across 4 datasets, comparing 10 MIL strategies, 6
self-supervised methods with 4 backbones, 4 foundation models, and various
pathology-adapted techniques. Furthermore, we introduce 4 instance-based MIL
methods never used before in the pathology domain. Through these extensive
experiments, we show that with a good SSL feature extractor, simple
instance-based MILs, with very few parameters, obtain similar or better
performance than complex, state-of-the-art (SOTA) embedding-based MIL methods,
setting new SOTA results on the BRACS and Camelyon16 datasets. Since simple
instance-based MIL methods are naturally more interpretable and explainable to
clinicians, our results suggest that more effort should be put into
well-adapted SSL methods for WSI rather than into complex embedding-based MIL
methods.
