Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).

翻译：计算病理学（CPath）是一门交叉学科，其通过发展计算方法来分析和建模医学组织病理学图像。CPath的主要目标是开发数字化诊断的基础设施与工作流程，作为临床病理学的辅助计算机辅助诊断（CAD）系统，从而推动癌症诊断与治疗的变革性转变——这正是CPath工具主要应对的领域。随着深度学习与计算机视觉算法的持续发展，以及数字病理学数据流动性的增强，CPath目前正经历范式转变。尽管针对癌症图像分析已涌现大量工程与科学成果，但这些算法在临床实践中的采纳与整合仍存在显著差距。这引发了一个关键问题：CPath当前的研究方向与趋势究竟为何？本文系统综述了800余篇论文，涵盖从问题设计到应用实施全流程所面临的挑战。我们通过分析关键工作与难题，将每篇论文归类至模型卡片，以勾勒CPath领域的研究现状。我们希望这能帮助学界定位相关成果，并促进对该领域未来方向的理解。简言之，我们将CPath的发展视为一个需紧密衔接的阶段性循环，以应对这一多学科交叉科学所特有的挑战。我们从数据中心、模型中心与应用中心三个视角审视该循环，最后指出现存挑战，并为其未来技术发展与临床整合提供方向（https://github.com/AtlasAnalyticsLab/CPath_Survey）。