Rethinking pathology image analysis through shuffling

Area of Science:

• Computational pathology
• Artificial intelligence in medicine
• Digital pathology

Background:

• Pathological examination is the gold standard for cancer diagnosis.
• Current AI methods face challenges in capturing multi-scale tumor morphology heterogeneity.
• Accurate analysis requires understanding complex relationships between biological structures.

Purpose of the Study:

• To introduce a novel framework, the PAthoentity Shuffle Strategy (PASS), for pathological image analysis.
• To enhance AI's ability to learn from both local and global morphological features.
• To improve the accuracy and generalizability of AI models in cancer diagnosis.

Main Methods:

• PASS explicitly models pathoentities (cells, glands, tissues) and their hierarchical relationships.
• Controlled shuffling of pathoentities within and across samples enriches relational structure for neural networks.
• Theoretical analysis demonstrates PASS achieves error bounds comparable to state-of-the-art methods.

Main Results:

• PASS demonstrates consistent performance gains across 10 diverse datasets (8 diseases, 9 organs, 4 magnifications).
• The framework shows robust generalization and scalability in various pathological contexts.
• PASS proved effective in a rapid onsite evaluation (ROSE) scenario, indicating clinical potential.

Conclusions:

• Pathoentity shuffling is an effective principle for pathological image analysis.
• PASS bridges biological insight and computational design for enhanced diagnostic modeling.
• This approach improves learning of morphological hierarchy and AI performance in cancer diagnosis.