Paper Info
Title
Development And Operation Of A Digital Platform For Sharing Pathology Image Data
Abstract
BackgroundArtificial 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. MethodsOver 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. ResultsA 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. DiscussionDue 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.ConclusionsDespite 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.
Year
DOI
Venue
2021
10.1186/s12911-021-01466-1
BMC MEDICAL INFORMATICS AND DECISION MAKING
Keywords
DocType
Volume
Digital pathology, Open platform, Artificial intelligence-assisted annotation, Medical image dataset
Journal
21
Issue
ISSN
Citations 
1
1472-6947
0
PageRank 
References 
Authors
0.34
0
20
Name
Order
Citations
PageRank
Yunsook Kang100.34
Yoo Jung Kim200.34
Seongkeun Park321.37
Gun Ro400.34
Choyeon Hong520.69
Hyungjoon Jang600.34
Sungduk Cho700.34
Won Jae Hong800.34
Dong Un Kang910.69
Jonghoon Chun1000.34
Kyoungbun Lee1121.03
Gyeong Hoon Kang1200.34
Kyoung Chul Moon1300.34
Gheeyoung Choe1400.34
Kyu Sang Lee1500.34
Jeong Hwan Park1600.34
Won-Ki Jeong1701.35
Se Young Chun1800.34
Peom Park1900.34
Jinwook Choi2000.34