Scholarlink

<p>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 <i>pathoentities</i>, 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Rethinking pathology image analysis through shuffling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

查看全文
  • Zeyu Liu,
  • Tianyi Zhang,
  • Brian K Chen,
  • Youdan Feng,
  • Shangqing Lyu,
  • Yanli Lei,
  • Nan Ying,
  • Yunlu Feng,
  • Yu Zhao,
  • Peng Zhang,
  • Fan Song,
  • Chenbin Ma,
  • Yufang He,
  • Kenji Kawaguchi,
  • Hwee Kuan Lee,
  • Yueming Jin,
  • Guanglei Zhang

摘要

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.