ArticleBrain sciences2022
Classification of Alzheimer's Disease Based on Weakly Supervised Learning and Attention Mechanism.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Weakly Supervised MRI-Based Classification of Alzheimer's Disease Using Clinical Pseudo-Labels.NeuroSci · 2026Article
- A deep residual attention-Recurrent model for early and multi-stage Alzheimer's disease detection.Frontiers in aging neuroscience · 2026Article
- Combining curriculum learning and weakly supervised attention for enhanced thyroid nodule assessment in ultrasound imaging.Quantitative imaging in medicine and surgery · 2025Article
- Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
The brain lesions images of Alzheimer's disease (AD) patients are slightly different from the Magnetic Resonance Imaging of normal people, and the classification effect of general image recognition technology is not ideal. Alzheimer's datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism. The weakly supervised data augmentation network is used as the basic model, the attention map generated by weakly supervised learning is used to guide the data augmentation, and an attention module with channel domain and spatial domain is embedded in the residual network to focus on the distinctive channels and spaces of images respectively. The location information enhances the corresponding features of related features and suppresses the influence of irrelevant features.The results show that the F1-score is 99.63%, the accuracy is 99.61%. Our model provides a high-performance solution for accurate classification of AD.
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Registered trials
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