Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model
Abstract
:1. Introduction
- Novelty—the Alzheimer’s disease anomaly analysis of PET images using a proposed unsupervised adversarially trained model with a unique feature extractor model. To the authors’ knowledge, there are no anomaly detection studies in Alzheimer’s disease cases using adversarial deep learning models.
- Effectiveness—the proposed model quantitatively and qualitatively outperforms the state-of-the-art models.
2. Related Anomaly Detection Works
3. Proposed Model
- The generator (G) learns the dataset distribution from the input image, encodes it into a latent vector, and reconstructs the image by upsampling. The uniqueness of the generator is that the encoder uses a parallel model that is comprised of a convolutional pipeline (CNN) and a dilated convolutional end network (DCN) that is 8 layers deep, each layer uses convolutional filters, a Rectified linear unit (ReLU) activation function, and a batch normalization operation. After two identical layers, a max-pooling operation is used for spatial dimension reduction and doubling the depth of the tensor. A latent vector of the input image is then generated.The DCN is eight layers deep, each layer uses convolutional filters with a dilation factor of 2, a ReLU (Rectified linear units) activation function, and a batch normalization operation. After 2 identical layers, a max-pooling operation is used for spatial dimension reduction and increasing the depth of the tensor. A latent vector of the input image is then generated. Concatenation of these features gives the optimal feature vector of the input image [58]. The class activation map for the given input image is shown in Figure 3.
- The discriminator (D) predicts the class of the input (whether it is fake or not) based on learned features. The discriminator generally uses an encoder-type architecture.
4. Experimental Environment and Results
4.1. The Dataset
4.2. Training the Model
- Contextual Loss: To learn the distribution of the dataset, normalization is applied to the input x and the output x̂. This helps the generation of contextually similar images from the normal samples and is proven to produce less blurry images than normalization [54]. The loss formula is given below as:
- Adversarial Loss: Taken from [28], this loss ensures that the generator G can reconstruct an image x as realistically as possible while the discriminator D can differentiate between the normal and fake images. The task is to minimize this objective for G and maximize it for D to achieve the min-max equilibrium where it is defined as:
- Latent Loss: This loss is used in obtaining the latent representations of the input x and the generated output x̂ as similar as possible. This ensures that the network can produce similar latent representations for sampling. Using the concatenated features (x) and the fully connected layer of the discriminator (x̂). The loss becomes:
4.3. Model Evaluation
5. Conclusions
- Among the three loss functions, the importance of each loss function can be evaluated. A genetic algorithm or a grid search can be used to assign weights for each loss function to observe its effect on the model.
- The depth of the parallel model can be altered by using other parameter search algorithms. A deeper model may increase the computational cost while improving the accuracy.
- The skip connections used in autoencoders have been showing promising results [57]. The possibility and feasibility of skip connections will be investigated to further improve the performance of the model.
- Probable ways to improve the AUC score further will be investigated.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AD | Alzheimer’s Disease |
MCI | Mild Cognitive Impairment |
NC | Normal Control |
A | Amyloid beta |
PET | Positron emission tomography |
MRI | Magnetic Resonance Imaging |
MMSE | Mini Mental State Examination |
CDR | Clinical Dementia Rating |
GANs | Generative Adversarial Networks |
DCGAN | Deep Convolutional Generative Adversarial Networks |
AnoGAN | Anomaly GAN |
EGBAD | Efficient GAN-Based Anomaly Detection |
BiGAN | Bidirectional GAN |
sMRI | structural Magnetic Resonance Imaging |
fMRI | functional Magnetic Resonance Imaging |
CNN | Convolutional Neural Network |
ReLU | Rectified Linear Units |
ROI | Region of Interest |
DCN | Dilated Convolutional Network |
AUC | Area Under the Curve |
FID | Fréchet Inception Distance |
FPR | False Positive Rate |
TPR | True Positive Rate |
SVM | Support Vector Machine |
SGD | Stochastic Gradient Descent |
ADNI | Alzheimer’s Disease Neuroimaging Initiative |
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Group | NC | MCI | AD | Total |
---|---|---|---|---|
No. of subjects | 148 | 83 | 25 | 256 |
Gender (M/F) | 82/66 | 45/38 | 15/10 | |
No. of Images | 14,208 | 7968 | 2400 | 24,576 |
Age, mean ± SD | 75.9 ± 4.5 | 74.8 ± 7.1 | 76.5 ± 6.7 | |
CDR, mean ± SD | 0.0 ± 0.0 | 0.5 ± 0.0 | 0.8 ± 0.2 | |
MMSE, mean ± SD | 29.1 ± 1.0 | 27.2 ± 1.6 | 23.4 ± 2.0 |
Training | Testing | Total | |||||
---|---|---|---|---|---|---|---|
Cases | AD | MCI | NC | AD | MCI | NC | |
AD-NC | 11,366 | 2400 | 2842 | 16,608 | |||
MCI-NC | 11,366 | 2842 | 2842 | 17,050 | |||
AD-MCI | 6374 | 2400 | 1594 | 10,368 |
Performance | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|
Parallel Model | 96.03 | 95.70 | 93.73 | 94.67 |
DenseNet169 [59] | 95.44 | 93.98 | 92.31 | 94.20 |
Inception-V3 [60] | 90.06 | 88.73 | 87.12 | 89.44 |
ResNet50 [61] | 89.41 | 86.81 | 87.29 | 88.52 |
VGG16 [62] | 85.13 | 83.98 | 82.31 | 85.01 |
Model | AD-NC | MCI-NC | AD-MCI |
---|---|---|---|
GANomaly [29] | 59.31 | 57.33 | 48.34 |
EGBAD [32] | 58.46 | 60.31 | 54.71 |
AnoGAN [23] | 55.71 | 52.93 | 48.11 |
Skip-GANomaly [56] | 71.42 | 67.02 | 68.56 |
Ablation-Conv. | 68.71 | 65.34 | 61.25 |
Ablation-Dilated Conv. | 60.82 | 54.02 | 52.40 |
Proposed Model | 75.21 | 70.84 | 71.85 |
Models | AD | MCI | NC |
---|---|---|---|
GANomaly [29] | 18.216 | 17.353 | 17.885 |
EGBAD [32] | 20.815 | 21.621 | 19.134 |
AnoGan [23] | 19.908 | 17.991 | 21.759 |
Skip-GANomaly [56] | 11.892 | 12.516 | 11.908 |
Ablation Conv. | 16.175 | 18.023 | 17.659 |
Ablation Dilated Conv. | 21.762 | 18.895 | 21.342 |
Proposed Model | 9.481 | 10.024 | 10.858 |
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Baydargil, H.B.; Park, J.-S.; Kang, D.-Y. Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model. Appl. Sci. 2021, 11, 2187. https://doi.org/10.3390/app11052187
Baydargil HB, Park J-S, Kang D-Y. Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model. Applied Sciences. 2021; 11(5):2187. https://doi.org/10.3390/app11052187
Chicago/Turabian StyleBaydargil, Husnu Baris, Jang-Sik Park, and Do-Young Kang. 2021. "Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model" Applied Sciences 11, no. 5: 2187. https://doi.org/10.3390/app11052187
APA StyleBaydargil, H. B., Park, J.-S., & Kang, D.-Y. (2021). Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model. Applied Sciences, 11(5), 2187. https://doi.org/10.3390/app11052187