Relation-induced multi-modal shared representation learning for Alzheimer’s disease diagnosis
Abstract
The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods.
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Ning et al. achieved 0.976 AUC and 0.969 accuracy for AD vs. NC on ADNI using MRI and PET.
“| Ning et al. [72] | magnetic resonance imaging (MRI) and positron emission tomography (PET) | MRI, PET | ADNI | 0.976 (AUC: AD vs. NC) 0.969 (ACC: AD vs. NC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [72]
Hason et al. achieved accuracy 0.822 on ADReSS using MFCC speech features.
“| Hason et al. [72] | MFCC | speech | ADReSS | Accuracy: 0.822 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [72]
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- Authors
- Ning, Z., Xiao, Q., Feng, Q., Chen, W., & Zhang, Y.
- Affiliations
- Southern Medical University (CN)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2021-06
- Volume
- 40
- Issue
- 6
- Pages
- 1632-1645
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3134105939
- Cited by
- 106
- References
- 41
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 26 · 2023: 23 · 2022: 12 · 2021: 2
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61971213, 61671230)
- Basic and Applied Basic Research Foundation of Guangdong Province (No.2019A1515010417)
- Guangdong Provincial Key Laboratory of Medical Image Processing (2020B1212060039)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease diagnosis, MRI-PET fusion, overfitting, dimensionality reduction, ADNI
- MeSH terms
- Alzheimer Disease, Humans, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Positron-Emission Tomography, Neuroimaging
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