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Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease
Bo Cheng, Mingxia Liu, Dinggang Shen, Zuoyong Li, Daoqiang Zhang, Alzheimer’s Disease Neuroimaging Initiative.. Neuroinformatics, 2017-04
Topic: Brain imaging, connectivity and assessment
Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer's Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multi-domain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D., & Alzheimer’s Disease Neuroimaging Initiative. (2017). Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics. https://doi.org/10.1007/s12021-016-9318-5
Vancouver
Cheng B, Liu M, Shen D, Li Z, Zhang D, Alzheimer’s Disease Neuroimaging Initiative. Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease. Neuroinformatics. 2017. doi:10.1007/s12021-016-9318-5. PMID: 27928657.
BibTeX
@article{cheng2017-27928657,
author = {Cheng, Bo and Liu, Mingxia and Shen, Dinggang and Li, Zuoyong and Zhang, Daoqiang and {Alzheimer’s Disease Neuroimaging Initiative}},
title = {{Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease}},
journal = {Neuroinformatics},
year = {2017},
doi = {10.1007/s12021-016-9318-5},
pmid = {27928657},
url = {https://doi.org/10.1007/s12021-016-9318-5}
}
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Key findings
- The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods.
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Study details
- Participants807
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / 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, multicenter collaboration and free to read, so those were left out: it earned 27 of the 56 points we could judge it on, which is 48 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Neuroinformatics) |
| 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 | 2 / 5 | 2017 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 807 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 27928657) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 27 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 27 of the 56 points we could judge it on is 48 out of 100. |
| Total | 48 / 100 | — |
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