Source-linked AI summary
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
Adrien Payan, Giovanni Montana
TL;DR
The paper addresses MRI-based prediction of Alzheimer’s disease and related disease status. It combines sparse autoencoders with convolutional neural networks, emphasizing whole-image 3D convolutions, and finds that 3D architectures generally outperform 2D architectures in the reported comparisons, although the margin is small and superiority over other studies is not definitive.
Problem
The study examines how neuroimaging and machine-learning methods can improve prediction of Alzheimer’s disease status from MRI scans.
Method
The authors combine sparse autoencoders with convolutional neural networks and compare whole-image 3D convolutions with 2D convolutions on slices.
Results
3D architectures perform better for the 3-way comparison and two binary comparisons, with no noticeable difference for AD versus HC.
Takeaways & Limitations
The experiments indicate that 3D convolutions have the potential to capture local 3D patterns and boost classification performance by a small margin.
Takeaways & Limitations
Interpreting the results is difficult because of the nature of the deep neural-network architectures.
Abstract
from arXiv · showhide
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.
1 INTRODUCTION
Alzheimer’s disease prediction from MRI is an active research area, motivated by the potential for early detection and improved diagnostic performance. The paper develops deep-learning classifiers using sparse autoencoders and whole-image 3D convolutions to distinguish healthy, AD, and MCI brains.
- AD is the most common type of dementia, while MCI involves noticeable but mild changes in thinking and increases risk of developing AD.
- MRI enables detailed 3D brain imaging for early AD detection, motivating continued development of automated prediction methods.Prior machine-learning approaches, including support vector machines, independent component analysis, and penalized regression, have been applied to neuroimages.
- The proposed algorithm combines sparse autoencoders with convolutional neural networks to classify MRI scans by disease status.The study evaluates three-way HC-versus-AD-versus-MCI classification and three binary comparisons.
- Whole-image 3D convolutions are compared with 2D convolutions applied to MRI slices.The authors identify 3D convolutions as the main novelty and report better performance than 2D convolutions in their experiments.
2 EXPERIMENTAL DATA
The experiments use MRI data from ADNI, an ongoing multicenter study focused on Alzheimer’s disease biomarkers. The dataset contains 755 patients in each of three classes: AD, MCI, and HC.
- The dataset contains 755 patients in each of three classes: AD, MCI, and HC.
3 DEEP NEURAL NETWORKS
The paper combines sparse autoencoders with 3D convolutional networks to learn localized MRI features and classify whole-brain scans. The architecture uses learned filters, convolution, pooling, and fully connected layers.
- Sparse Autoencoders: The two-stage approach first trains a sparse autoencoder, then initializes a convolutional network with its learned filters.The autoencoder learns filters from randomly selected 3D MRI patches for subsequent convolution operations.
- Sparse Autoencoders: Sparse constraints keep most hidden-unit activations near zero, helping the overcomplete autoencoder learn useful filters instead of the identity function.The sparsity penalty encourages representations that may disentangle factors controlling MRI-image variability.
- Sparse Autoencoders: The autoencoder is trained on 100,000 unrolled 5×5×5 patches using 150 hidden units, split into training, validation, and test sets.The split is 80,000 training patches, 10,000 validation patches, and 10,000 test patches.
- 3D Convolutional Networks: 3D convolution applies learned filters across localized image regions, producing feature maps whose spatial dimensions depend on the input and filter sizes.A 5×5×5 filter produces a 64×91×75 feature map from the stated input dimensions.
- 3D Convolutional Networks: The network uses local connectivity and shared parameters to detect features across positions while reducing the number of parameters.Pooling further reduces feature-map size and provides robustness to small image distortions such as translations.
4 A BRIEF REVIEW OF MRI CLASSIFIERS FOR AD
MRI-based AD classification has used SVMs, feature extraction, penalized regression, and increasingly deep learning. Prior deep-learning studies differ from this paper mainly in whether they use 3D convolutions.
- SVMs have been widely used to discriminate subjects with AD from structural MRI data.Reported studies include linear-kernel SVMs applied to grey matter signatures and SVMs coupled with ICA or penalized regression.
- Direct comparison across studies is difficult because datasets and preprocessing protocols differ.
- Related studies combine SVM classification with feature extraction using grey matter signatures, independent component analysis, or penalized regression.
- Deep-learning approaches have used autoencoders to learn convolutional filters from 2D MRI or natural-image patches before softmax classification.
- Other work has used stacked autoencoders to pre-train a fully connected network, without convolution operations.
5 RESULTS
The models were evaluated on held-out MRI examples and compared across classification tasks and convolution dimensionality. Results favored 3D convolutions for most comparisons, while interpretation remained difficult because of the network architectures.
- Experimental setup: 1,731 examples trained the networks, 306 supported validation and early stopping, and 228 formed the test set.The test set evaluated performance on unseen examples.
- Experimental setup: Table 1 reports selected AD and MCI classifiers with sample sizes and test-set accuracy, where AD, MCI, and HC denote disease, mild impairment, and healthy control.AD vs. HC denotes binary classification between Alzheimer’s disease and healthy controls.
- Interpretation: Figure 3 displays randomly selected examples from each class after convolution with the fourth 3D sparse-autoencoder basis at the 32nd slice.
- Results: 3D convolutions performed better than 2D convolutions for the 3-way, AD vs. MCI, and HC vs. MCI comparisons.For AD vs. HC, the two approaches showed no noticeable difference.
- Interpretation: Interpreting the results is difficult because of the deep neural-network architectures.
- Results: Table 2 compares 2D and 3D model accuracy on the test set across one 3-way and three binary classification tasks.
6 CONCLUSION
The paper evaluates a classification system combining sparse autoencoders with convolutional neural networks, including a comparison between 2D and 3D convolutions. The 3D approach showed potential for a small performance boost, while further tuning and fine-tuning could improve the systems.
- The system combines sparse autoencoders and convolutional neural networks to classify MRI scans, while comparing 2D and 3D convolutions.
- 3D convolutions may capture local 3D patterns that boost classification performance, but the observed margin was small.
- More exhaustive hyper-parameter searches could improve both architectures.
- The convolutional layer was autoencoder-pretrained but not fine-tuned, and fine-tuning may improve performance at substantially higher training complexity.