AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis
Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders characterized by the progressive accumulation of amyloid-beta plaques and tau protein tangles in the brain. Due to the lack of a cure for Alzheimer’s disease, early and accurate diagnosis of AD is cr...
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2025-01-01
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| author | Gia Minh Hoang Nhat Hoang Tran Thi Hoai Thu Hoang Tien Trong Nghia Jae Gwan Kim |
| author_facet | Gia Minh Hoang Nhat Hoang Tran Thi Hoai Thu Hoang Tien Trong Nghia Jae Gwan Kim |
| author_sort | Gia Minh Hoang |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders characterized by the progressive accumulation of amyloid-beta plaques and tau protein tangles in the brain. Due to the lack of a cure for Alzheimer’s disease, early and accurate diagnosis of AD is crucial for effective early interventions to slow disease progression. Magnetic Resonance Imaging (MRI) has emerged as a promising modality for early diagnosis, providing detailed insights into brain structure alterations associated with AD. However, domain shift due to variations in imaging protocols and data distribution among national cohorts remains a challenge for the application of MRI in clinical diagnosis. To address this issue, we propose AttCORAL, a novel Domain-Adaptive Attention Network for Early Alzheimer’s Disease Diagnosis, integrating attention mechanisms with Correlation Alignment (CORAL) loss to effectively mitigate domain discrepancy, enhancing the model’s robustness and generalization. We evaluate AttCORAL on two large-scale MRI datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers, and Lifestyle Study (AIBL)—which differ in acquisition protocols and demographics. While these datasets provide a valuable basis for cross-cohort validation, we acknowledge that further multi-cohort studies are necessary to fully assess global generalizability. To ensure the reliability of our approach, we apply Grad-CAM to visualize the pathological brain regions most informative for our model’s predictions. Experimental results demonstrate that AttCORAL significantly outperforms current state-of-the-art studies, highlighting its effectiveness in early diagnosis of AD across diverse imaging domains. |
| format | Article |
| id | doaj-art-b4808bf64f384a089ccbbf0eec89bf9e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-b4808bf64f384a089ccbbf0eec89bf9e2025-08-20T03:30:49ZengIEEEIEEE Access2169-35362025-01-011310950310951210.1109/ACCESS.2025.358078011039780AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease DiagnosisGia Minh Hoang0https://orcid.org/0000-0002-8494-0096Nhat Hoang1Tran Thi Hoai Thu2https://orcid.org/0009-0006-1851-4095Hoang Tien Trong Nghia3https://orcid.org/0000-0002-0054-7212Jae Gwan Kim4https://orcid.org/0000-0002-1010-7712Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of KoreaKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USADepartment of Neurology, Military Hospital 175, Ho Chi Minh City, VietnamDepartment of Neurology, Military Hospital 175, Ho Chi Minh City, VietnamDepartment of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of KoreaAlzheimer’s disease (AD) is one of the most common neurodegenerative disorders characterized by the progressive accumulation of amyloid-beta plaques and tau protein tangles in the brain. Due to the lack of a cure for Alzheimer’s disease, early and accurate diagnosis of AD is crucial for effective early interventions to slow disease progression. Magnetic Resonance Imaging (MRI) has emerged as a promising modality for early diagnosis, providing detailed insights into brain structure alterations associated with AD. However, domain shift due to variations in imaging protocols and data distribution among national cohorts remains a challenge for the application of MRI in clinical diagnosis. To address this issue, we propose AttCORAL, a novel Domain-Adaptive Attention Network for Early Alzheimer’s Disease Diagnosis, integrating attention mechanisms with Correlation Alignment (CORAL) loss to effectively mitigate domain discrepancy, enhancing the model’s robustness and generalization. We evaluate AttCORAL on two large-scale MRI datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers, and Lifestyle Study (AIBL)—which differ in acquisition protocols and demographics. While these datasets provide a valuable basis for cross-cohort validation, we acknowledge that further multi-cohort studies are necessary to fully assess global generalizability. To ensure the reliability of our approach, we apply Grad-CAM to visualize the pathological brain regions most informative for our model’s predictions. Experimental results demonstrate that AttCORAL significantly outperforms current state-of-the-art studies, highlighting its effectiveness in early diagnosis of AD across diverse imaging domains.https://ieeexplore.ieee.org/document/11039780/Alzheimer’s diseasedeep learningmagnetic resonance imagingdomain adaptation |
| spellingShingle | Gia Minh Hoang Nhat Hoang Tran Thi Hoai Thu Hoang Tien Trong Nghia Jae Gwan Kim AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis IEEE Access Alzheimer’s disease deep learning magnetic resonance imaging domain adaptation |
| title | AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis |
| title_full | AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis |
| title_fullStr | AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis |
| title_full_unstemmed | AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis |
| title_short | AttCORAL: Domain-Adaptive Attention Networks for Early Alzheimer’s Disease Diagnosis |
| title_sort | attcoral domain adaptive attention networks for early alzheimer x2019 s disease diagnosis |
| topic | Alzheimer’s disease deep learning magnetic resonance imaging domain adaptation |
| url | https://ieeexplore.ieee.org/document/11039780/ |
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