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Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease
Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ et al.. IEEE transactions on bio-medical engineering, 2014-11-20
Topic: Brain imaging, connectivity and assessment
The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.
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APA 7th
Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., et al. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE transactions on bio-medical engineering. https://doi.org/10.1109/tbme.2014.2372011
Vancouver
Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE transactions on bio-medical engineering. 2015. doi:10.1109/tbme.2014.2372011. PMID: 25423647.
BibTeX
@article{liu2015-25423647,
author = {Liu, S. and Cai, W. and Che, H. and Pujol, S. and Kikinis, R. and Feng, D. and Fulham, M. J. and others},
title = {{Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease}},
journal = {IEEE transactions on bio-medical engineering},
year = {2015},
doi = {10.1109/tbme.2014.2372011},
pmid = {25423647},
url = {https://doi.org/10.1109/tbme.2014.2372011}
}
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Key findings
- Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data.
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Study details
- Evidence strengthLow
How this study scored 36/100
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. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 21 of the 58 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on bio-medical engineering) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 1 / 5 | 2015 — within 20 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 25423647) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 21 of the 58 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
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