[An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].
The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.
Authors
- Ruimeng Yang (ORCID: https://orcid.org/0000-0003-2768-9429)
- Xin Zhen (ORCID: https://orcid.org/0000-0002-5037-2801)
- Jianxiao Xie (ORCID: https://orcid.org/0009-0005-5700-8868)
- Canrong Wu (ORCID: https://orcid.org/0000-0002-2272-3838)
- Wen‐Zhao Zhong (ORCID: https://orcid.org/0000-0002-8917-8635)
- Yuankui Wu (ORCID: https://orcid.org/0000-0002-5140-4860)
- Y. Xu
- Li Wang (ORCID: https://orcid.org/0000-0003-2068-7535)
Institutions
- Nanfang Hospital (CN)
- Guangzhou First People's Hospital (CN)
- Guangzhou Medical University Cancer Hospital (CN)
- Southern Medical University (CN)
- South China University of Technology (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- PubMed
- Published
- 2024-08-20
- DOI
- https://doi.org/10.12122/j.issn.1673-4254.2024.08.15
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
Funders
- National Natural Science Foundation of China