Tizhoosh H R, Pantanowitz Liron
Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
Department of Pathology, School of Medicine, University of Pittsburgh, PA, USA.
J Pathol Inform. 2024 Apr 4;15:100375. doi: 10.1016/j.jpi.2024.100375. eCollection 2024 Dec.
Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.
组织病理学的病理图像可以从配备摄像头的显微镜或全切片扫描仪中获取。利用相似度计算基于这些图像对患者进行匹配在研究和临床环境中具有巨大潜力。搜索技术的最新进展允许对不同原发部位的组织形态进行隐式量化,便于比较,并能够在与经过整理的诊断和治疗病例数据库进行比较时推断诊断结果,甚至可能推断预后情况,并对新患者进行预测。在本文中,我们全面回顾了组织病理学图像搜索技术的最新发展,为在工作中寻求有效、快速且高效图像搜索方法的计算病理学研究人员提供了一个简要概述。