Combining Unsupervised and Supervised Deep Learning for Alzheimer's Disease Detection by Fractional Anisotropy Imaging

1. Combining Unsupervised and Supervised Deep
Learning for Alzheimer’s Disease Detection by
Fractional Anisotropy Imaging
Giovanna Castellano, Eufemia Lella, Valerio Longo, Giuseppe Placidi,
Matteo Polsinelli, Gennaro Vessio

2. Context
❏ Alzheimer’s disease (AD) is a progressive neurological disease that may
require early diagnosis for effective treatment
❏ Diffusion tensor imaging (DTI) measures the integrity of the white matter
fiber tract and aids in the early diagnosis of AD
❏ Fractional anisotropy (FA), derived from DTI, reflects the altered diffusion
of water in the brain
2

3. Motivations
❏ FA image analysis is challenging for traditional methods and requires
extensive manual intervention
❏ Deep learning techniques are promising for AD detection using FA by
automatically extracting meaningful features
❏ However, deep learning models typically require a large labeled dataset to
be effective in medical imaging applications
3

4. Contributions
❏ Proposed approach: combination of unsupervised and supervised deep
learning for AD detection using FA imaging
❏ Methodology: pre-train a 3D convolutional autoencoder on unlabeled data
and fine-tune a 3D CNN using the learned representations
❏ Advantage of 3D CNNs: learns spatial features directly from volumetric
data, unlike traditional 2D CNNs
❏ Benefits of unsupervised pre-training: improve performance and reduce
reliance on large labeled datasets with difficult-to-collect labels
4

5. Data
❏ Our study was based on the OASIS-3 longitudinal dataset (1098 subjects)
and used the IXI dataset (400 subjects) to increase the sample size
❏ The scans were analyzed for errors and inconsistencies
❏ Physicians’ judgments and diagnoses were used to determine AD status
❏ Dataset imbalance (98% negative samples) was resolved by
undersampling the negative class
❏ Discarded scans and IXI data reserved for autoencoder pre-training
5

6. Preprocessing
❏ On-the-fly data augmentation was also used to increase the sample size
❏ Preprocessing involved scaling the scans, extracting a brain mask, and
calculating fractional anisotropy
❏ The images have been cropped to a size of 96×96×64 pixels
❏ FA values ranged from 0 to 1, requiring no additional scaling
6

7. Method
7

8. Method
8

9. Method
9

10. Experimental setup
❏ The classification model was evaluated using stratified 10-fold
cross-validation
❏ The same subject was absent in training and test sets to avoid
over-optimistic results
❏ The evaluation procedure involved training the autoencoder on unlabeled
samples and fine-tuning the encoder for classification
❏ We also experimented with a denoising autoencoder, which reconstructed
clean input from noisy input (Gaussian noise)
10

11. Results
11
Without
pre-training
Pre-training
(autoencoder)
Pre-training
(denoising)
Accuracy 0.634 (0.09) 0.667 (0.05) 0.660 (0.06)
Sensitivity 0.675 (0.20) 0.719 (0.19) 0.683 (0.23)
Specificity 0.604 (0.25) 0.627 (0.18) 0.643 (0.22)
AUC 0.739 (0.08) 0.749 (0.05) 0.750 (0.06)

12. Conclusions
❏ Promising results on the recently released OASIS-3 dataset
❏ Unsupervised pre-training could improve robustness and generalization
❏ Future work includes validation on more extensive and diverse datasets,
interpretation of learned representations, extension to other diseases, and
exploration of additional DTI modalities
❏ There are limitations due to the relative non-specificity of MRI and DTI for
diagnosing AD…
12

13. Thanks for
your attention!
Gennaro Vessio
Computational Intelligence Lab,
Department of Computer Science,
University of Bari Aldo Moro
gennaro.vessio@uniba.it