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

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Authors
Kang, Yunsook; Kim, Yoo Jung; Park, Seongkeun; Ro, Gun; Hong, Choyeon; Jang, Hyungjoon; Cho, Sungduk; Hong, Won Jae; Kang, Dong Un; Chun, Jonghoon; Lee, Kyoungbun; Kang, Gyeong Hoon; Moon, Kyoung Chul; Choe, Gheeyoung; Lee, Kyu Sang; Park, Jeong Hwan; Jeong, Won-Ki; Chun, Se Young; Park, Peom; Choi, Jinwook
Issue Date
2021-04-03
Publisher
BMC
Citation
BMC Medical Informatics and Decision Making. 2021 Apr 03;21(1):114
Keywords
Digital pathologyOpen platformArtifcial intelligence-assisted annotationMedical image dataset
Abstract
Background
Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists.

Methods
Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists workload, AI-assisted annotation was established in collaboration with university AI teams.

Results
A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models.

Discussion
Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition.

Conclusions
Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.
ISSN
1472-6947
Language
English
URI
https://hdl.handle.net/10371/174439
DOI
https://doi.org/10.1186/s12911-021-01466-1
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College of Medicine/School of Medicine (의과대학/대학원)Biomedical Engineering (의공학전공)Journal Papers (저널논문_의공학전공)
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