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Abstract：
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. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
摘要：
计算病理学（CPath）是一门跨学科科学，它增强了分析和建模医学组织病理学图像的计算方法的发展。CPath的主要目标是开发数字诊断的基础设施和工作流程，作为临床病理学的辅助CAD系统，促进癌症诊断和治疗中的转化变化，主要由CPath工具解决。随着深度学习和计算机视觉算法的不断发展，以及数字病理学数据流动的便利性，目前，CPath正在见证范式转变。尽管癌症图像分析引入了大量的工程和科学工作，在临床实践中采用和整合这些算法仍有相当大的差距。这提出了一个关于CPath的方向和趋势的重要问题。在本文中，我们提供了800多篇论文的全面回顾，以解决在问题设计中所面临的挑战，所有的应用和实现观点。我们通过检查在CPath中布局当前景观所面临的关键作品和挑战，将每篇论文编目到模型卡中。我们希望这有助于社区找到相关作品，并促进对该领域未来方向的理解。简而言之，我们监督CPath的发展阶段周期，这些阶段需要紧密地联系在一起，以应对与这种多学科科学相关的挑战。我们从以数据为中心的不同角度来概述这个周期，以模型为中心，和以应用程序为中心的问题。最后，我们概述了剩余的挑战，并为CPath的未来技术发展和临床整合提供了方向。有关此调查审查文件的最新信息以及对原始模型卡存储库的访问，请参阅GitHub。此草案的更新版本也可以从arXiv找到。
