Abstract：
Traditional histopathology, characterized by manual quantifications and assessments, faces challenges such as low-throughput and inter-observer variability that hinder the introduction of precision medicine in pathology diagnostics and research. The advent of digital pathology allowed the introduction of computational pathology, a discipline that leverages computational methods, especially based on deep learning (DL) techniques, to analyze histopathology specimens. A growing body of research shows impressive performances of DL-based models in pathology for a multitude of tasks, such as mutation prediction, large-scale pathomics analyses, or prognosis prediction. New approaches integrate multimodal data sources and increasingly rely on multi-purpose foundation models. This review provides an introductory overview of advancements in computational pathology and discusses their implications for the future of histopathology in research and diagnostics.
摘要：
传统的组织病理学,以手动量化和评估为特征，面临的挑战，如低通量和观察者间的差异，阻碍了在病理学诊断和研究中引入精准医学。数字病理学的出现允许引入计算病理学，一个利用计算方法的学科，特别是基于深度学习(DL)技术，分析组织病理学标本.越来越多的研究表明，基于DL的模型在病理学上的许多任务表现令人印象深刻，如突变预测，大规模的病理组学分析，或预后预测。新方法集成了多模式数据源，并且越来越依赖多用途基础模型。这篇综述提供了计算病理学进展的介绍性概述，并讨论了它们对组织病理学在研究和诊断中的未来的意义。
