A Digital Pathology Platform for Artificial Intelligence Data Sharing (Preprint)

Abstract

<sec> <title>BACKGROUND</title> High-quality learning materials are needed for artificial intelligence (AI) development, but are not practically available; this situation is especially poor in the medical field. In particular, annotating medical images (e.g., annotation for tumor area by pathologists) is massive as well as expensive, and subject to privacy protection. These are major limitations for AI developers to approach and reproduce medical image data. </sec> <sec> <title>OBJECTIVE</title> This study aimed to reduce barriers for AI researchers to access medical image datasets by collating and sharing high-quality medical images with pathologists, and to find applicable ways to apply diagnostic AI assistance to reduce the pathologists’ workload. </sec> <sec> <title>METHODS</title> Pathology slides of tumors of five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) from histologically confirmed cases were selected for this study. After scanning the slides to obtain whole slide digital images, the patient information was de-identified, and annotation for the tumor area was performed by the pathologist. Next, an AI-assisted annotation process was used in parallel to improve the annotation workload of pathologists and to draw complex lesion boundaries more accurately. This allowed all the data to include the annotations confirmed by experienced pathologists, and to be used as an AI learning dataset. </sec> <sec> <title>RESULTS</title> A web-based data-sharing platform for AI learning was built, and was unveiled in 2019. In total, 3,100 massive datasets of 5 organ carcinomas were shared through this platform, and were accessible to all researchers. The platform had the advantage that users could search data visually and intuitively; except for commercial purposes, all researchers made free use of the provided dataset for their research. Finally, the platform also provided five image data pre-processing algorithms that could help AI modeling learners. </sec> <sec> <title>CONCLUSIONS</title> We built and operated a web-based data-sharing platform for AI researchers providing a high-quality digital pathology dataset personally annotated by pathologists. We hope that our experience will help researchers who want to build such a platform in future, by sharing issues gained from collecting and sharing these valuable data. </sec>

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