Contrastive Learning for Prediction of Alzheimer’s Disease Using Brain 18F-FDG PET
Open accessAbstract
Brain 18F-FDG PET images are commonly-known materials for effectively predicting Alzheimer's disease (AD). However, the data volume of PET is usually insufficient, which is unfavorable to train an accurate AD prediction networks. Furthermore, the PET image is noisy with low signal-to-noise ratio, and simultaneously the feature (metabolic abnormality) used for predicting AD in PET image is not always obvious. Therefore, a contrastive-based learning method is proposed to address the challenges of PET image inherently possessed. Firstly, the slices of 3D PET image are amplified by cropping the image of anchors (i.e., an augmented version of the same image) to generate extended training data. Meanwhile, contrastive loss is adopted to enlarge inter-class feature distances and reduce intra-class feature differences using subject fuzzy labels as supervised information. Secondly, we construct a double convolutional hybrid attention module to enhance the network to learn different perceptual domains where two convolutional layers with different convolutional kernels ($7\times 7$ and $5\times 5$) are constructed. Moreover, we recommend a diagnosis mechanism by analyzing the consistency of predicted result for PET slices alone with clinical neuropsychological assessment to achieve a better AD diagnosis. The experimental results show that the proposed method outperforms the state-of-the-arts for brain 18F-FDG PET images, and hence demonstrate the advantage of the method in effectively predicting AD.
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Transformer is valuable for processing and modeling diverse modalities of medical data, encompassing clinical texts, medical images, and time series data.
“In the realm of medical diagnosis, A transformer proves valuable for processing and modeling diverse modalities of medical data, encompassing clinical texts, medical images, and time series data [59,60,61].”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [60]
Chen et al. achieved 0.9193 accuracy for AD vs. NC vs. MCI and 0.9287 AUC on ADNI using optimized anchor data from brain 18F-FDG PET slices.
“| Chen et al. [60] | optimized anchor data from brain 18F-FDG PET slices | PET | ADNI | 0.9193 (ACC: AD vs. NC vs. MCI) 0.9287 (AUC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [60]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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Self-citation
- Authors
- Chen, Y., Wang, H., Zhang, G., Liu, X., Huang, W., Han, X., Li, X., Martin, M., & Tao, L.
- Affiliations
- Anhui University (CN)
- Deakin University (AU)
- The University of Winnipeg (CA)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2023-04
- Volume
- 27
- Issue
- 4
- Pages
- 1735-1746
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4312247626
- Cited by
- 36
- References
- 30
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 5 · 2023: 8
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61972001, 62106005)
- Natural Science Foundation of Anhui Province (1908085MF209)
- Natural Science Foundation for the Higher Education Institutions of Anhui Province (2022AH050091)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease prediction, 18F-FDG PET, contrastive learning, brain glucose metabolism, image augmentation, fuzzy labels, hybrid attention module, convolution kernels, neuropsychological assessment, low signal-to-noise ratio
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