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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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Published on: July 11, 2025

497

Development and operation of a digital platform for sharing pathology image data.

Yunsook Kang1, Yoo Jung Kim2, Seongkeun Park3

  • 1Department of Biomedical Engineering, Seoul National University Hospital, Seoul, Republic of Korea.

BMC Medical Informatics and Decision Making
|April 4, 2021
PubMed
Summary

A new platform provides digital pathology data for artificial intelligence (AI) research, addressing the growing need for quality medical datasets. This system facilitates data sharing between AI developers and pathologists, overcoming operational challenges.

Keywords:
Artificial intelligence-assisted annotationDigital pathologyMedical image datasetOpen platform

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Area of Science:

  • Digital pathology
  • Artificial intelligence in medicine
  • Medical data sharing

Background:

  • Artificial intelligence (AI) research heavily relies on data availability.
  • The medical field's increasing adoption of AI drives demand for high-quality medical data.
  • Development of a digital pathology data platform for AI researchers is described.

Purpose of the Study:

  • To develop and describe a platform for providing and sharing digital pathology data.
  • To highlight challenges in operating a sustainable data-sharing platform with pathologists.
  • To meet the increasing demand for quality medical data in AI research.

Main Methods:

  • A dataset of over 3000 pathological slides from five organs in tumor cases was curated.
  • Pathologists annotated tumor areas on digitized slides to create ground truth for AI training.
  • AI-assisted annotation was implemented to reduce pathologist workload, in collaboration with AI teams.

Main Results:

  • A web-based data sharing platform was launched in 2019, featuring 3100 images.
  • The platform provides 5 pre-processing algorithms for AI researchers.
  • The platform facilitates the sharing of massive pathological image data.

Conclusions:

  • The medical image sharing platform effectively meets AI developers' demand for quality data, despite development challenges.
  • Patient consent during data acquisition is crucial for international data sharing platforms due to privacy regulations.
  • The study aims to guide future researchers in creating more effective and accessible medical data platforms.