The optimal labelling method for artificial intelligence-assisted polyp detection in colonoscopy
Labelling methodology affect the predictability of AI model in polyp detection. Extending the bounding box to 20% of the polyp margin would result in the best polyp detection predictive model based on AUC data. It is mandatory to establish a standardized way in colon polyp labeling for comparison of the precision of different AI models.
Authors
- Yen‐Po Wang (ORCID: https://orcid.org/0000-0002-3769-0020)
- Ying-Chun Jheng
- Ching‐Liang Lu (ORCID: https://orcid.org/0000-0001-9335-7241)
- Ming‐Chih Hou
Institutions
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
Publication Details
- Journal
- Journal of the Formosan Medical Association
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1016/j.jfma.2024.12.022
- Citations
- 6
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.70
Funders
- Ministry of Science and Technology of the People's Republic of China
- Ministry of Science and Technology, Taiwan
- Taipei Veterans General Hospital