Alzheimer's Disease Classification Using Distilled Multi-Residual Network
Sponsor
This work was sponsored in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010109001, in part by Guangdong Natural Science Foundation under Grant 2020A1515011409, in part by Construction Project of Regional Innovation Capability and Support Guarantee System in Guangdong Province under Grant 2021A1414030004, in part by Provincial Agricultural Science and technology innovation and Extension project of Guangdong Province under Grant 2022KJ147.
Published In
Applied Intelligence
Document Type
Citation
Publication Date
9-15-2022
Abstract
Early human intervention is crucial for diagnosing Alzheimer’s Disease (AD), since AD is irreversible and leads to progressive impairment of memory. In recent years, Convolutional Neural Networks (CNNs) have achieved dramatic breakthroughs in AD diagnosis. However, existing CNNs have difficulties in extracting subtle contextual information because of their structural limitations, i.e., it is difficult to extract discriminative features of several regions, such as hippocampus, parietal, temporal lobe tissues, and so on. In addition, current networks have difficulty in classifying imaging features with imbalanced data categories. Some loss functions can alleviate the above problems to some extent, but they are affected by outliers and trapped in local optimum easily. To address this issue, a Distilled Multi-Residual Network (DMRNet) is proposed for the early diagnosis of AD. The DMRNet consists of three main components: 1) Dense Connection Block, 2) Focus Attention Block, and 3) Multi-scale Fusion Module. The dense features are extracted by the Dense Connection Block while the local abnormal regions are refined by the Focus Attention Block and Multi-scale Fusion Module. Besides, to explore the hidden knowledge between each feature, a dilated classifier with self-distillation is proposed to ensemble several items pertaining to feature knowledge from feature space. Finally, the Remix Balance Sampler (RBS) is proposed to alleviate the influences of outliers. The proposed DMRNet is evaluated on baseline sMRI scans of the ADNI dataset. The result of experiments demonstrated that the proposed DMRNet not only achieves 7.15% greater accuracy than state-of-the-art methods but also successfully identifies some AD-related regions.
Rights
© 2022 Springer Nature Switzerland AG. Part of Springer Nature.
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DOI
10.1007/s10489-022-04084-0
Persistent Identifier
https://archives.pdx.edu/ds/psu/38627
Citation Details
El Kortbawi, M., Moug, D. M., Ziotopoulou, K., DeJong, J. T., & Boulanger, R. W. (2022). Axisymmetric Simulations of Cone Penetration in Biocemented Sands. Journal of Geotechnical and Geoenvironmental Engineering, 148(11), 04022098.