Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI
The developed RA-MIL model has the potential to serve as a non-invasive auxiliary tool for preoperative LNM prediction, offering visual interpretability regarding informative MRI slices and regions in LNM-positive patients.
Authors
- Fengying Qin
- Fengyu Cong (ORCID: https://orcid.org/0000-0003-0058-2429)
- Yue Dong (ORCID: https://orcid.org/0000-0001-5795-716X)
- Xiaofeng Wang (ORCID: https://orcid.org/0000-0001-8212-6931)
- Ranran Wang (ORCID: https://orcid.org/0000-0001-6846-4483)
- Hongming Xu (ORCID: https://orcid.org/0009-0009-6255-263X)
- Shan Jin (ORCID: https://orcid.org/0000-0003-2544-0531)
- Xinyu Hao (ORCID: https://orcid.org/0009-0003-8134-1180)
Institutions
- Cleveland Clinic (US)
- Dalian University of Technology (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
- University of Jyväskylä (FI)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2024-10-06
- DOI
- https://doi.org/10.1002/acm2.14547
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Liaoning Province
- Fundamental Research Funds for the Central Universities