Development and application of deep learning-based diagnostics for pathologic diagnosis of gastric endoscopic submucosal dissection specimens
The deep learning model demonstrated satisfactory performance in tissue segmentation and high accuracy in detecting tumors and submucosal invasion. This model can potentially serve as a screening tool in the histopathological diagnosis of ESD specimens.
Authors
- Soomin Ahn (ORCID: https://orcid.org/0000-0002-1979-4010)
- Hyuk Lee (ORCID: https://orcid.org/0000-0003-4271-7205)
- Yiyu Hong (ORCID: https://orcid.org/0000-0001-6138-7083)
- Jun Haeng Lee (ORCID: https://orcid.org/0000-0002-5272-1841)
- Yunjoo Cho (ORCID: https://orcid.org/0000-0003-1309-2015)
- Kyoung‐Mee Kim (ORCID: https://orcid.org/0000-0002-1162-9205)
- Inwoo Hwang (ORCID: https://orcid.org/0000-0002-7749-3929)
- Ji Min Na (ORCID: https://orcid.org/0000-0003-4330-6598)
- Sujin Park (ORCID: https://orcid.org/0000-0002-1211-8394)
- Jae J. Kim (ORCID: https://orcid.org/0000-0003-2068-7287)
- Byung‐Hoon Min (ORCID: https://orcid.org/0000-0001-8048-361X)
Institutions
- Samsung Medical Center (KR)
- Infrafrontier (DE)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Gastric Cancer
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1007/s10120-025-01612-y
- Citations
- 2
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Sungkyunkwan University
- Korea Health Industry Development Institute