Rethinking pathology image analysis through shuffling

Abstract

Pathological examination is the current gold standard in cancer diagnosis, yet artificial intelligence (AI) methods still struggle to capture the multi-scale heterogeneity of tumor morphology across patients, tissues, and magnifications. Here, we introduce the PAthoentity Shuffle Strategy (PASS), a principled framework that explicitly models pathoentities, the critical biological structures such as cells, glands, and tissues, and their hierarchical relationships. By controlled shuffling of pathoentities within and across samples, PASS enriches the relational structure available to neural networks, encouraging them to learn both local homogeneity and global heterogeneity. We provide theoretical analysis showing that PASS achieves error bounds comparable to state-of-the-art methods, supporting shuffling as a generalizable computational principle rather than a heuristic. Extensive evaluation on 10 datasets spanning 8 diseases, 9 organs, and 4 magnification levels demonstrates consistent performance gains, robust generalization, and scalability across diverse pathological contexts. Importantly, PASS further shows translational value in a rapid onsite evaluation (ROSE) scenario in gastroenterology, highlighting its potential for clinical deployment. Overall, this study establishes pathoentity shuffling as an effective principle for pathological image analysis, bridging biological insight and computational design to enhance diagnostic modeling and morphological hierarchy learning.