progress of machine learning technology, region extraction and object recognition for images have become possible. In particular, deep learning enables the analysis of several images with the same criteria. Furthermore, this method has succeeded in recognizing an object by determining the object features that a human is unaware of. Currently, there is a demand for the development of a diagnosis assistance system for medical images using this technology. In medical image analysis, “machine learning techniques for a small number of images” and “image quality variation due to shooting conditions” are problems to be addressed. We believe that these problems can be solved by the “construction of an image database,” “standardization of imaging methods,” and “standardization of sample processing procedures.” Prof. Rajendra Singh (Mt. Sinai School of Medicine) delivered a presentation, entitled “The Promise of AI and Digital Pathology – Hype or Real?” The key to building true clinically relevant models and algorithms that can predict patient outcomes, management, or prognosis is having access to a large amount of patient data. Gaining access to high-quality big data sources, especially open access, will require shared data governance, accuracy, and dependability. Open-access platforms with deidentification or anonymization will need to obey these principles to support such deliverables. Web-based platforms will enable collaborative annotations to be made on the data, which will also need to be verified, to produce viable models for real clinical practice. Dr. Tomoharu Kiyuna (NEC Corporation) delivered a presentation entitled “On the Stability of an AI-Based Cancer Detection System and Its Influencing Factors.” He reviewed an AI-based cancer detection process and pointed out that little attention has been paid to the stability of AI-based histological diagnosis. The stability of the AI output, i.e., fluctuation of output values and reproducibility, can be affected by several factors, such as the specimen staining quality and imaging process characteristics (focusing, light source, etc.). The instability arises not only from the input image but also from the analysis algorithm. As long as the AI decision process is based on some “threshold” of the values obtained by evaluating the “cancerous-ness” of the histopathological image, it is inevitable that the final decision fluctuates near the decision boundary. Prof. Masahiro Yamaguchi (Tokyo Institute of Technology) delivered a talk on “Standardization of Color in Pathology Image Analysis: Its Importance and Challenges.” Since color variation is caused by the staining and scanning processes, the color variation issues must be addressed in digital pathology. Previously, we developed a whole-slide imaging (WSI) image analysis system that utilized machine learning, in which the color correction module played an important role. Nevertheless, there was an argument that color variation can be learned by AI. If the purpose of AI is to only make a decision based on the visual observation that is currently made by pathologists, then AI might deal with the color variation. However, for AI to contribute to the progress of pathology and the The International Workshop on Harmonization and Standardization of Digital Pathology Image (DPI) was held on April 4, 2019, at the National Cancer Center (NCC), Tokyo, Japan. Experts on artificial intelligence (AI)-based DPI analysis from the USA and Japan discussed problems relating to the implementation of AIaided applications into real-world pathology practice. The participants included representatives of the Japan Agency of Medical Research and Development (AMED) and the Japanese Society of Pathology. During the meeting, standardization in color of DPI and DPI scanners was repeatedly demanded by various experts. This meeting report provides the summaries of presentations by the experts and introduces the consensus of this workshop. In the opening remarks, Dr. Atsushi Ochiai (NCC), the chairman, introduced his recent work on an AI-aided pathological diagnosis system based on gastrointestinal biopsy specimens [1, 2] and raised two critical issues to be solved for improving the reproducibility of DPI analysis. First, differences in staining procedures would result in different colors of the same specimen. Second, differences in DPI scanners would result in different digital images of the same slide. He demonstrated that these differences could reduce the accuracy and reproducibility of the AI-aided applications. The chairman opened this workshop by asking: “What kind of standardization should be achieved for implementing AI-aided systems into real-world pathology practice?” Dr. Hideo Yokota (RIKEN) delivered a presentation entitled “Development of AI Systems to Assist Image Diagnosis.” With the Received: July 16, 2019 Accepted: August 11, 2019 Published online: November 8, 2019

中文翻译：

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会议报告：2019年4月4日在东京召开的数字病理图像协调与标准化国际研讨会

机器学习技术的进步，图像的区域提取和物体识别已经成为可能。特别是，深度学习可以使用相同的标准分析多张图像。此外，该方法通过确定人类不知道的物体特征，成功地识别了物体。目前，需要开发使用该技术的医学图像诊断辅助系统。在医学图像分析中，“少量图像的机器学习技术”和“由于拍摄条件导致的图像质量变化”是需要解决的问题。我们认为，这些问题可以通过“图像数据库的构建”、“成像方法的标准化”、“样本处理程序的标准化”来解决。Rajendra Singh 教授 (Mt. 西奈医学院）发表了题为“人工智能和数字病理学的前景——炒作还是真实？”的演讲。构建能够预测患者结果、管理或预后的真正临床相关模型和算法的关键是能够访问大量患者数据。获得对高质量大数据源的访问，尤其是开放访问，将需要共享数据治理、准确性和可靠性。具有去标识化或匿名化的开放访问平台需要遵守这些原则以支持此类可交付成果。基于网络的平台将能够对数据进行协作注释，这些数据也需要进行验证，以生成用于实际临床实践的可行模型。博士。Tomoharu Kiyuna（NEC 公司）发表了题为“基于 AI 的癌症检测系统的稳定性及其影响因素”的演讲。他回顾了一个基于人工智能的癌症检测过程，并指出很少有人关注基于人工智能的组织学诊断的稳定性。AI 输出的稳定性，即输出值的波动和再现性，会受到多种因素的影响，例如标本染色质量和成像过程特性（聚焦、光源等）。不稳定性不仅来自输入图像，还来自分析算法。只要 AI 决策过程是基于通过评估组织病理学图像的“癌性”获得的值的某个“阈值”，最终决策在决策边界附近波动是不可避免的。Masahiro Yamaguchi 教授（东京工业大学）发表了题为“病理图像分析中颜色标准化：其重要性和挑战”的演讲。由于颜色变化是由染色和扫描过程引起的，因此必须在数字病理学中解决颜色变化问题。之前，我们开发了一个利用机器学习的全幻灯片成像 (WSI) 图像分析系统，其中色彩校正模块发挥了重要作用。尽管如此，有人认为人工智能可以学习颜色变化。如果 AI 的目的只是根据目前病理学家的视觉观察做出决定，那么 AI 可能会处理颜色变化。然而，2019 年 4 月 4 日，在日本东京国立癌症中心 (NCC) 举办了数字病理图像 (DPI) 协调和标准化国际研讨会。来自美国和日本的基于人工智能 (AI) 的 DPI 分析专家讨论了与将 AIaided 应用程序实施到现实世界病理实践相关的问题。参与者包括日本医学研究与开发机构 (AMED) 和日本病理学会的代表。会议期间，多位专家反复要求DPI和DPI扫描仪的色彩标准化。本次会议报告对专家的发言进行了总结，并介绍了本次研讨会的共识。在开幕词中，Atsushi Ochiai 博士（NCC），主席介绍了他最近在基于胃肠道活检标本的 AI 辅助病理诊断系统方面的工作 [1, 2] 并提出了两个需要解决的关键问题，以提高 DPI 分析的可重复性。首先，染色程序的不同会导致同一标本的颜色不同。其次，DPI 扫描仪的差异会导致同一幻灯片的不同数字图像。他证明了这些差异会降低人工智能辅助应用程序的准确性和可重复性。主席在研讨会开始时问道：“将人工智能辅助系统应用到现实世界的病理实践中应该实现什么样的标准化？” Hideo Yokota 博士 (RIKEN) 发表了题为“开发辅助图像诊断的 AI 系统”的演讲。随着收到：7月16日，