ArticleIEEE transactions on cybernetics2022
Attention-Guided Hybrid Network for Dementia Diagnosis With Structural MR Images.
Article in IEEE transactions on cybernetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- [Research on the application of convolution neural network in the diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Pooled it
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Generative Adversarial Networks Based on Fine-Grained Image Recognition for the Progression Prediction of Progressive Mild Cognitive Impairment.Interdisciplinary sciences, computational life sciences · 2026Article
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Machine learning for Alzheimer's disease progression under extreme class imbalance.Frontiers in neuroscience · 2026Article
- Learning discrete structures for cancer radiomics.APL bioengineering · 2025Article
- An imaging and genetic-based deep learning network for Alzheimer's disease diagnosis.Frontiers in aging neuroscience · 2025Article
- Development and validation of a multimodal deep learning framework for vascular cognitive impairment diagnosis.iScience · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.Journal of translational medicine · 2024Article
- Deep joint learning of pathological region localization and Alzheimer's disease diagnosis.Scientific reports · 2023Article
- Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- AMSF: attention-based multi-view slice fusion for early diagnosis of Alzheimer's disease.PeerJ. Computer science · 2023Article
- Interpretable deep learning-based hippocampal sclerosis classification.Epilepsia open · 2022Article
- Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.IEEE transactions on neural networks and learning systems · 2022Article
- An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.Medical image analysis · 2022Article
- Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network.Frontiers in aging neuroscience · 2022Article
- Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data.Applied intelligence (Dordrecht, Netherlands) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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. 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. Specifically, we first design a backbone fully convolutional network to automatically localize the discriminative brain regions in a weakly supervised manner. Using the identified disease-related regions as spatial attention guidance, we further develop a hybrid network to jointly learn and fuse multilevel sMRI features for CAD model construction. Our proposed method was evaluated on three public datasets (i.e., ADNI-1, ADNI-2, and AIBL), showing superior performance compared with several state-of-the-art methods in both tasks of AD diagnosis and MCI conversion prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.