An interpretable artificial intelligence system for measuring the size of small polyps (< 10 mm).
PolypM, an interpretable system for colonic polyp size estimation, was developed to mitigate measurement ambiguity and facilitate decision-making regarding surgical intervention and postoperative surveillance timing.
Authors
- J. F. Chen (ORCID: https://orcid.org/0000-0002-7108-6214)
- Yu Wu (ORCID: https://orcid.org/0000-0002-1680-8253)
- Xiaowei Liu (ORCID: https://orcid.org/0000-0002-1193-4277)
- Bin Liu (ORCID: https://orcid.org/0000-0001-9388-0198)
- Yanliang Hou
- Fujun Li (ORCID: https://orcid.org/0000-0001-6032-1242)
- Shizhe Li (ORCID: https://orcid.org/0000-0002-2665-6564)
- Cong Cai (ORCID: https://orcid.org/0000-0001-7911-6118)
- Yong Li (ORCID: https://orcid.org/0009-0006-2641-1002)
- Guanghui Lian
Institutions
- Central South University (CN)
- Xiangyang Central Hospital (CN)
- Xiangya Hospital Central South University (CN)
- The Eighth Hospital of Xi'an (CN)
Publication Details
- Journal
- PubMed
- Published
- 2025-10-23
- DOI
- https://doi.org/10.17235/reed.2025.11539/2025
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00