Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET
Abstract
:1. Introduction
- A transformer model is suggested for the identification of MCI progression. The model expands upon the ViT backbone by utilizing 18F-FDG-PET and self-supervised learning to tackle the issue of MCI progression and disease identification.
- To address the issue of inadequate data in the field of brain imaging, we suggested a cross-domain transfer learning technique. We used ViT as the backbone with DINO.
- In the MCI recognition, experimental data show that the proposed method can achieve more competitive outcomes than current models. The model accuracy levels with the ADNI dataset were 92.31%, which is higher than the baseline’s ViT approach. Finally, we visualized important metabolic brain regions, which can assist the physician for proper analysis of MCI.
2. Materials and Methods
2.1. Dataset
2.2. FDG-PET Image Acquisition and Preprocessing
2.3. Self-Supervised Learning
2.4. Vision Transformer (ViT)
2.5. 18F-FDG-PET Feature Learning with ViT-Dino
2.6. Classifiers
2.7. Training Setup
2.8. Evaluation Matrixs
3. Results
3.1. Classification Performance on 18F-FDG-PET
3.2. Ablation Study
3.3. Performance Comparison with State-of-Art Methods
3.4. Pathological Attention Regions on FDG-PET by ViT DINO
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Groups | Gender (M/F) | Education | Age (Years) | MoCA | MMSE | CDR | APOEƐ4 |
---|---|---|---|---|---|---|---|
MCI-s | 130/115 # | 16.3 ± 2.7 | 72.3 ± 7.5 * | 23.7 ± 2.4 * | 28.0 ± 1.7 * | 1.1 ± 0.5 * | 43.1% |
MCI-c | 119/105 | 16.2 ± 2.1 | 74.1 ± 7.3 | 21.1 ± 2.7 | 26.3 ± 2.1 | 2.3 ± 1.0 | 74.0% |
Model | Classifiers | ACC % (mean ± std) | SEN % (mean ± std) | SPE % (mean ± std) | PRE % (mean ± std) | Recall % (mean ± std) | F1-Score % (mean ± std) |
---|---|---|---|---|---|---|---|
ViT-S | 82.37 ± 1.29 | 75.51 ± 2.01 | 88.71 ± 1.03 | 83.87 ± 1.45 | 84.33 ± 3.02 | 83.30 ± 1.05 | |
ViT-B | 81.75 ± 2.13 | 85.38 ± 3.14 | 79.85 ± 1.71 | 83.54 ± 2.52 | 82.47 ± 2.45 | 82.71 ± 1.71 | |
ViT-L | 78.93 ± 1.07 | 67.83 ± 2.73 | 90.97 ± 1.07 | 81.75 ± 2.59 | 78.83 ± 3.74 | 79.34 ± 2.15 | |
DINO ViT-B | KNN | 88.36 ± 1.91 | 81.71 ± 2.47 | 95.08 ± 1.32 | 89.15 ± 3.11 | 88.31 ± 2.14 | 88.25 ± 1.72 |
SVM | 85.24 ± 3.73 | 92.92 ± 1.01 | 78.06 ± 3.45 | 86.01 ± 2.47 | 85.49 ± 1.75 | 85.21 ± 1.04 | |
ELM | 92.31 ± 1.07 | 90.21 ± 3.37 | 95.50 ± 2.15 | 93.10 ± 1.88 | 92.95 ± 2.31 | 93.92 ± 1.33 |
Model | Patch Size | Classifiers | ACC % (mean ± std) | SEN % (mean ± std) | SPE % (mean ± std) | PRE % (mean ± std) | Recall % (mean ± std) | F1-Score % (mean ± std) |
---|---|---|---|---|---|---|---|---|
DINO ViT-B | 8 | KNN | 87.49 ± 1.23 | 95.93 ± 3.11 | 79.61 ± 2.15 | 88.45 ± 1.33 | 87.77 ± 1.04 | 87.46 ± 1.87 |
SVM | 86.47 ± 1.55 | 78.16 ± 2.45 | 94.23 ± 1.07 | 87.44 ± 1.58 | 86.2 ± 2.78 | 86.31 ± 1.95 | ||
ELM | 91.56 ± 1.03 | 86.75 ± 1.47 | 96.06 ± 1.56 | 91.98 ± 1.19 | 91.4 ± 1.74 | 91.51 ± 1.51 | ||
16 | KNN | 88.36 ± 1.91 | 81.71 ± 2.47 | 95.08 ± 1.32 | 89.15 ± 3.11 | 88.31 ± 2.14 | 88.25 ± 1.72 | |
SVM | 85.24 ± 3.73 | 92.92 ± 1.01 | 78.06 ± 3.45 | 86.01 ± 2.47 | 85.49 ± 1.75 | 85.21 ± 1.04 | ||
ELM | 92.95 ± 1.07 | 90.21 ± 3.37 | 95.50 ± 2.15 | 93.10 ± 1.88 | 92.95 ± 2.31 | 93.92 ± 1.33 | ||
32 | KNN | 82.62 ± 3.01 | 93.52 ± 1.41 | 72.43 ± 4.75 | 84.15 ± 2.13 | 82.98 ± 1.71 | 82.58 ± 1.37 | |
SVM | 81.31 ± 2.45 | 64.16 ± 5.78 | 97.33 ± 1.21 | 85.07 ± 2.04 | 80.74 ± 3.41 | 80.58 ± 1.78 | ||
ELM | 86.84 ± 1.58 | 75.45 ± 3.71 | 97.47 ± 1.03 | 88.74 ± 1.23 | 86.64 ± 1.59 | 86.57 ± 1.79 |
Study | Modality | Method | ACC | SEN | SPE |
---|---|---|---|---|---|
Nozadi et al. [55] | FDG-PET | RF | 72.5 | 79.2 | 69.9 |
Bae et al. [56] | MRI | ResNet | 86.1 | 84 | 74.8 |
Zhu et al. [28] | MRI | Dual attention multi-instance deep learning network | 80.2 | 77.1 | 82.6 |
MRI | ViT-S | 83.27 | 85.07 | 81.48 | |
Hoang et al. [34] | ViT-B | 80.67 | 79.1 | 82.22 | |
ViT-L | 72.86 | 74.63 | 71.11 | ||
Duan J et al. [57] | FDG-PET | CNN | - | 81.63 | 85.19 |
Choi and Jin et al. [58] | FDG-PET | Deep Learning | 84.2 | 81.0 | 87.0 |
Our | FDG-PET | DINO-ELM | 92.95 | 90.21 | 95.50 |
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Khatri, U.; Kwon, G.-R. Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET. Bioengineering 2023, 10, 1225. https://doi.org/10.3390/bioengineering10101225
Khatri U, Kwon G-R. Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET. Bioengineering. 2023; 10(10):1225. https://doi.org/10.3390/bioengineering10101225
Chicago/Turabian StyleKhatri, Uttam, and Goo-Rak Kwon. 2023. "Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET" Bioengineering 10, no. 10: 1225. https://doi.org/10.3390/bioengineering10101225