Novel multi-task learning for Alzheimer’s stage classification using hippocampal MRI segmentation, feature fusion, and nomogram modeling
This study demonstrates that integrating radiomics, deep learning, and clinical data from hippocampal MRI enables accurate and interpretable classification of AD stages. The proposed framework is robust, generalizable, and clinically actionable, representing a scalable solution for AD diagnostics.
Authors
- Wenxiu Hu (ORCID: https://orcid.org/0000-0001-6215-6527)
- Qingyun Du (ORCID: https://orcid.org/0000-0003-4615-2029)
- Lisi Wei (ORCID: https://orcid.org/0000-0002-5265-9259)
- Dawei Wang (ORCID: https://orcid.org/0000-0002-6651-4261)
- Guang Zhang (ORCID: https://orcid.org/0000-0002-5233-071X)
Institutions
- Shandong Management University (CN)
- Shandong Provincial QianFoShan Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1186/s40001-025-03204-z
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Shandong Province