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Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data
Zhou T, Thung KH, Liu M, Shi F, Zhang C, Shen D.. Medical image analysis, 2019-12-28
Topic: Risk, prevention and care in population studies
Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities.
Key findings
- However, there are at least four common issues associated with the existing fusion methods.
- First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities.
- Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression.
- Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other.
Why it matters
This paper describes a multi-modal latent space ensemble SVM classifier for early dementia diagnosis using neuroimaging data. It is a methods development study, so it cannot show that any treatment would prevent or cure Alzheimer's disease.
Study details
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Medical image analysis) |
| Journal's citation record | 2 / 4 | Medical Image Analysis: journal h-index 221 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 109 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 13.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 8.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 42 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 4 / 4 | Last-listed author Dinggang G. Shen: h-index 149 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of North Carolina at Chapel Hill: institution h-index 1,674 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31927474) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.media.2019.101630
- PubMed: 31927474
- Original source →
- Free full text: PubMed Central →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Zhou, T., Thung, K. H., Liu, M., Shi, F., Zhang, C., & Shen, D. (2020). Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Medical image analysis. https://doi.org/10.1016/j.media.2019.101630
Vancouver
Zhou T, Thung KH, Liu M, Shi F, Zhang C, Shen D. Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Medical image analysis. 2020. doi:10.1016/j.media.2019.101630. PMID: 31927474.
BibTeX
@article{zhou2020-31927474,
author = {Zhou, T. and Thung, K. H. and Liu, M. and Shi, F. and Zhang, C. and Shen, D.},
title = {{Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data}},
journal = {Medical image analysis},
year = {2020},
doi = {10.1016/j.media.2019.101630},
pmid = {31927474},
url = {https://doi.org/10.1016/j.media.2019.101630}
}
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