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.
Zeyu Liu, Tianyi Zhang, Brian K. Chen et al.· npj Biomedical Innovations· 0 citations
Digital pathology supports whole-slide imaging, remote review, and computational analysis. Most pathology AI systems, however, remain restricted to predefined tasks. Agentic architectures coordinate perception models, language-based reasoning, external tools, and feedback-dependent actions, but their clinical evidence is derived mainly from retrospective benchmarks and research prototypes. We review agentic systems in computational pathology using an operational taxonomy based on dynamic control flow, inference-time tool selection, and knowledge integration. We assess architectures, enabling technologies, and applications in diagnosis, prognosis, and therapeutic support. Reported gains are difficult to attribute to agentic organization because studies differ in backbones, training data, and inference budgets. We therefore emphasize validation scope, computational cost, workflow integration, hallucination and security risks, regulatory requirements, patient preferences, and the conditions under which specialist non-agentic models remain preferable. Agentic architectures have established technical feasibility, but not clinical benefit. Translation should prioritize verifiable tasks, matched comparisons, prospective and external validation, lifecycle governance, and interfaces that preserve pathologist oversight.
Xin-Yu Lu, Qian-Kun Li, Yak Gao et al.· Journal of Translational Med...· 0 citations
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