Meeting Report: The International Workshop on Harmonization and Standardization of Digital Pathology Image, Held on April 4, 2019 in Tokyo

Abstract

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