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Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images
Lian C, Liu M, Liu M, Pan Y, Shen D.. IEEE transactions on cybernetics, 2022-04-05
Topic: Risk, prevention and care in population studies
Deep-learning methods (especially convolutional neural networks) using structural magnetic resonance imaging (sMRI) data have been successfully applied to computer-aided diagnosis (CAD) of Alzheimer's disease (AD) and its prodromal stage [i.e., mild cognitive impairment (MCI)]. As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction. Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework.
Key findings
- As it is practically challenging to capture local and subtle disease-associated abnormalities directly from the whole-brain sMRI, most of those deep-learning approaches empirically preselect disease-associated sMRI brain regions for model construction.
- Considering that such isolated selection of potentially informative brain locations might be suboptimal, very few methods have been proposed to perform disease-associated discriminative region localization and disease diagnosis in a unified deep-learning framework.
- However, those methods based on task-oriented discriminative localization still suffer from two common limitations, that is: 1) identified brain locations are strictly consistent across all subjects, which ignores the unique anatomical characteristics of each brain and 2) only limited local regions/patches are used for model training, which does not fully utilize the global structural information provided by the whole-brain sMRI.
- In this article, we propose an attention-guided deep-learning framework to extract multilevel discriminative sMRI features for dementia diagnosis.
Why it matters
This paper describes a deep learning method for diagnosing dementia and its prodromal stage from structural MRI scans. It is a computer-aided diagnostic approach and does not offer any treatment, cure, or prevention.
Study details
- Evidence strengthLow
How this study scored 38/100
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, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 24 of the 64 points we could judge it on, which is 38 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE transactions on cybernetics) |
| 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 | 3 / 5 | 2022 — within 6 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 (OpenAlex dates this DOI to 2020, the record says 2022; OpenAlex values not used) — 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 | 1 / 6 | Cited by 1 other studies in this library |
| 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 32721906) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 24 / 64 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 24 of the 64 points we could judge it on is 38 out of 100. |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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APA 7th
Lian, C., Liu, M., Pan, Y., & Shen, D. (2022). Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images. IEEE transactions on cybernetics. https://doi.org/10.1109/tcyb.2020.3005859
Vancouver
Lian C, Liu M, Pan Y, Shen D. Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images. IEEE transactions on cybernetics. 2022. doi:10.1109/tcyb.2020.3005859. PMID: 32721906.
BibTeX
@article{lian2022-32721906,
author = {Lian, C. and Liu, M. and Pan, Y. and Shen, D.},
title = {{Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images}},
journal = {IEEE transactions on cybernetics},
year = {2022},
doi = {10.1109/tcyb.2020.3005859},
pmid = {32721906},
url = {https://doi.org/10.1109/tcyb.2020.3005859}
}
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