A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training | Zendy

Sang Jun Park | Zendy; Joo Young Shin | Zendy; Sangkeun Kim | Zendy; Jaemin Son | Zendy; Kyu-Hwan Jung | Zendy; Kyu Hyung Park | Zendy

Open Access

A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training

Author(s) -

Sang Jun Park,

Joo Young Shin,

Sangkeun Kim,

Jaemin Son,

Kyu-Hwan Jung,

Kyu Hyung Park

Publication year - 2018

Publication title -

journal of korean medical science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.743

H-Index - 66

eISSN - 1598-6357

pISSN - 1011-8934

DOI - 10.3346/jkms.2018.33.e239

Subject(s) - fundus (uterus) , medical diagnosis , computer science , artificial intelligence , abnormality , reading (process) , image quality , categorical variable , algorithm , optometry , machine learning , image (mathematics) , medicine , ophthalmology , radiology , psychiatry , political science , law

This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

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