ArticleHuman brain mapping2022
A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- A Feature-Augmented Explainable Artificial Intelligence Model for Diagnosing Alzheimer's Disease from Multimodal Clinical and Neuroimaging Data.Diagnostics (Basel, Switzerland) · 2025Article
- A Meta-Learning-Based Ensemble Model for Explainable Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.Neuroinformatics · 2024Article
- Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.European radiology · 2023Article
- Real-time pneumonia prediction using pipelined spark and high-performance computing.PeerJ. Computer science · 2023Article
- A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.Human brain mapping · 2022Article
- A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion.Frontiers in aging neuroscience · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Recently, many sMRI-based deep learning methods have been developed for AD diagnosis. Some of these methods utilize neural networks to extract high-level representations on the basis of handcrafted features, while others attempt to learn useful features from brain regions proposed by a separate module. However, these methods require considerable manual engineering. Their stepwise training procedures would introduce cascading errors. Here, we propose the parallel attention-augmented bilinear network, a novel deep learning framework for AD diagnosis. Based on a 3D convolutional neural network, the framework directly learns both global and local features from sMRI scans without any prior knowledge. The framework is lightweight and suitable for end-to-end training. We evaluate the framework on two public datasets (ADNI-1 and ADNI-2) containing 1,340 subjects. On both the AD classification and mild cognitive impairment conversion prediction tasks, our framework achieves competitive results. Furthermore, we generate heat maps that highlight discriminative areas for visual interpretation. Experiments demonstrate the effectiveness of the proposed framework when medical priors are unavailable or the computing resources are limited. The proposed framework is general for 3D medical image analysis with both efficiency and interpretability.
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