Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI
Open accessAbstract
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few deep learning methods (e.g., convolutional neural networks, CNNs) have been proposed to learn task-oriented features from sMRI for AD diagnosis, and achieved superior performance than the conventional learning-based methods using hand-crafted features. However, these existing CNN-based methods still require the pre-determination of informative locations in sMRI. That is, the stage of discriminative atrophy localization is isolated to the latter stages of feature extraction and classifier construction. In this paper, we propose a hierarchical fully convolutional network (H-FCN) to automatically identify discriminative local patches and regions in the whole brain sMRI, upon which multi-scale feature representations are then jointly learned and fused to construct hierarchical classification models for AD diagnosis. Our proposed H-FCN method was evaluated on a large cohort of subjects from two independent datasets (i.e., ADNI-1 and ADNI-2), demonstrating good performance on joint discriminative atrophy localization and brain disease diagnosis.
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Lian et al. achieved 0.9 accuracy and 0.95 AUC for AD vs. NC on ADNI using original MRI scan features.
“| Lian et al. [70] | Original MRI scan feature | MRI | ADNI | 0.9 (ACC); 0.95 (AUC:AD vs. NC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [70]
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- Authors
- Lian, C., Liu, M., Zhang, J., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Shen, Dinggang
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020-04-01
- Volume
- 42
- Issue
- 4
- Pages
- 880-893
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- PMC
- PMC6588512
- OpenAlex
- W2906155095
- Cited by
- 544
- References
- 75
- Citations by year
- 2026: 38 · 2025: 73 · 2024: 94 · 2023: 100 · 2022: 75 · 2021: 87 · 2020: 49 · 2019: 28
- FWCI
- 32.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- UNC Libraries
- Funders
- NIH (EB008374, AG041721, AG042599, EB022880)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease diagnosis, structural MRI, multi-scale feature fusion, computer-aided diagnosis, neurodegenerative diseases, ADNI
- MeSH terms
- Supervised Machine Learning, Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Brain, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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