Classification of Alzheimer's disease based on multi-example learning and multi-scale feature fusion.
10.7507/1001-5515.202405035
- Author:
An ZENG
1
;
Zhifu SHUAI
1
;
Dan PAN
2
;
Jinzhi LIN
3
Author Information
1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, P. R. China.
2. School of Electronics and Information Engineering, Guangdong University of Technology and Education, Guangzhou 510665, P. R. China.
3. Department of Neurosurgery, The Second People's Hospital of Guangdong Province, Guangzhou 510310, P. R. China.
- Publication Type:Journal Article
- Keywords:
Alzheimer’s disease;
Multi-example learning;
Multi-scale;
Positional coding;
Voxel block
- MeSH:
Alzheimer Disease/diagnosis*;
Humans;
Neuroimaging/methods*;
Neural Networks, Computer;
Brain/diagnostic imaging*;
Magnetic Resonance Imaging;
Deep Learning;
Machine Learning
- From:
Journal of Biomedical Engineering
2025;42(1):132-139
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.
