Multi‐view sparse attention network for glioma survival risk prediction
The proposed method highlights that we effectively fuse multi-view non-invasive information, offering advantages in survival risk prediction and advancing the research on glioma prognosis.
Authors
- Hulin Kuang (ORCID: https://orcid.org/0000-0001-7341-9871)
- Yi Luo (ORCID: https://orcid.org/0000-0002-5488-4724)
- Jianhong Cheng (ORCID: https://orcid.org/0000-0001-6092-211X)
- Mengshen He (ORCID: https://orcid.org/0000-0001-9436-0805)
- Xinyu Li (ORCID: https://orcid.org/0009-0001-6178-3390)
- Jianxin Wang (ORCID: https://orcid.org/0000-0003-1516-0480)
Institutions
- Central South University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-03-25
- DOI
- https://doi.org/10.1002/mp.17774
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Program of Hunan Province
- National Key Research and Development Program of China