Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
While multiple instance learning (MIL) has shown to be a promising approach
for histopathological whole slide image (WSI) analysis, its reliance on
permutation invariance significantly limits its capacity to effectively uncover
semantic correlations between instances within WSIs. Based on our empirical and
theoretical investigations, we argue that approaches that are not
permutation-invariant but better capture spatial correlations between instances
can offer more effective solutions. In light of these findings, we propose a
novel alternative to existing MIL for WSI analysis by learning to restore the
order of instances from their randomly shuffled arrangement. We term this task
as cracking an instance jigsaw puzzle problem, where semantic correlations
between instances are uncovered. To tackle the instance jigsaw puzzles, we
propose a novel Siamese network solution, which is theoretically justified by
optimal transport theory. We validate the proposed method on WSI classification
and survival prediction tasks, where the proposed method outperforms the recent
state-of-the-art MIL competitors. The code is available at
https://github.com/xiwenc1/MIL-JigsawPuzzles.
