CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer’s disease
In conclusion, the performance of our model in various tasks has demonstrated its significant potential in the field of small-sample multimodal medical imaging classification, particularly AD classification. This advancement could significantly assist clinicians in effectively managing and intervening in the disease progression of patients with early-stage AD.
Authors
- Jinglong Wu (ORCID: https://orcid.org/0000-0002-6330-8235)
- Hidenao Fukuyama (ORCID: https://orcid.org/0000-0002-8421-2806)
- Xiaojie Yu (ORCID: https://orcid.org/0000-0002-2238-6552)
- Qi Li (ORCID: https://orcid.org/0000-0002-2716-449X)
- Jingyuan Liu (ORCID: https://orcid.org/0000-0001-5624-1258)
- Zhilin Zhang (ORCID: https://orcid.org/0000-0002-3084-8198)
- Toshiya Murai (ORCID: https://orcid.org/0000-0001-6550-8188)
Institutions
- Changchun University of Science and Technology (CN)
- Chinese Academy of Sciences (CN)
- Kyoto University (JP)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- BMC Geriatrics
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1186/s12877-025-05771-6
- Citations
- 6
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- National Natural Science Foundation of China
- Japan Society for the Promotion of Science