3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

Dhinagar, Nikhil J.; Thomopoulos, Sophia I.; Owens‐Walton, Conor; Stripelis, Dimitris; Ambite, José Luis; Steeg, Greg Ver; Weintraub, Daniel; Cook, Philip A.; McMillan, Corey T.; Thompson, Paul M.

doi:10.1117/12.2606297

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…With additional data - including longitudinal scans - the models could be used to predict progression of dementia or classification of other neurological diseases with limited publicly available data such as Parkinson’s disease. 4…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…The T1-w brain MRI volumes were pre-processed using a sequence of steps, 4 20 including nonparametric intensity normalization (N4 bias field correction), 21 ‘skull stripping’ for brain extraction, linear registration to a template with a 9 degrees of freedom transformation, and isometric voxel resampling to 2-mm resolution. The pre-processed T1-w images were of size 91×109×91.…”

Section: Datamentioning

confidence: 99%

“…2 Structural magnetic resonance imaging (MRI) based on standard anatomical T1-weighted (T1-w) is routinely used and accessible worldwide and does not subject the patient to ionizing radiation. In recent times with an increase in the available training data, deep learning and machine learning methods have shown great promise for various neuroimaging tasks such as brain age prediction, 3 diagnostic classification 4 and disease subtyping and staging, 5 based on detecting profiles of brain atrophy with T1-w MRI. In 2019, Wen et al 6 published a comprehensive review of over 30 papers that used convolutional neural networks (CNNs) for AD classification from anatomical MRI.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI

Dhinagar¹,

Thomopoulos²,

Rajagopalan³

et al. 2022

Preprint

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Deep neural networks show great promise for classifying brain diseases and making prognostic assessments based on neuroimaging data, but large, labeled training datasets are often required to achieve high predictive accuracy. Here we evaluated a range of transfer learning or pre-training strategies to create useful MRI representations for downstream tasks that lack large amounts of training data, such as Alzheimer's disease (AD) classification. To test our models, we analyzed 4,098 3D T1-weighted brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort and independently validated our proposed methods for detecting AD with an out-of-distribution test set of 600 scans from the Open Access Series of Imaging Studies (OASIS3) cohort. First, we trained 3D and 2D convolutional neural network (CNN) architectures. We tested combinations of multiple pre-training strategies based on (1) supervised, (2) contrastive learning, and (3) self-supervised learning - using pre-training data within versus outside the MRI domain. In our experiments, the 3D CNN pre-trained with contrastive learning provided the best overall results - when fine-tuned on T1-weighted scans for AD classification - outperformed the baseline by 2.8% when trained with all of the training data from ADNI. We also show test performance as a function of the training dataset size and the chosen pre-training method. Transfer learning offered significant benefits in low data regimes, with a performance boost of 7.7%. When the pre-trained model was used for AD classification, we were able to visualize an improved clustering of test subjects' diagnostic groups, as illustrated via a uniform manifold approximation (UMAP) projection of the high-dimensional model embedding space. Further, saliency maps indicate the additional activation regions in the brain scan using pre-training, that then maximally contributed towards the final prediction score.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI

Dhinagar¹,

Thomopoulos²,

Rajagopalan³

et al. 2022

Preprint

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“…Dementia affects more than 50 million people worldwide, and this number could exceed 152 million by 2050 [45] with Alzheimer's disease (AD) being the leading cause. Recently, deep learning has been applied to identify AD from structural brain MRI scans [10], [46]- [48].…”

Section: B Predicting Alzheimer's Diseasementioning

confidence: 99%

“…Images were preprocessed following the pipeline in [46]. First, images were reoriented using fslreorient2std (FSL v6.0.1), so to match the orientation of standard template images.…”

Section: B Predicting Alzheimer's Diseasementioning

confidence: 99%

Secure Federated Learning for Neuroimaging

Stripelis¹,

Gupta²,

Saleem³

et al. 2022

Preprint

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The amount of biomedical data continues to grow rapidly. However, the ability to collect data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns.We present a Secure Federated Learning architecture, MetisFL, which enables distributed training of neural networks over multiple data sources without sharing data. Each site trains the neural network over its private data for some time, then shares the neural network parameters (i.e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.Our architecture provides strong security and privacy. First, sample data never leaves a site. Second, neural parameters are encrypted before transmission and the community model is computed under fully-homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing membership attacks.We demonstrate this architecture in neuroimaging. Specifically, we investigate training neural models to classify Alzheimer's disease, and estimate Brain Age, from magnetic resonance imaging datasets distributed across multiple sites, including heterogeneous environments where sites have different amounts of data, statistical distributions, and computational capabilities.

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Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review

Zia‐ur‐Rehman,

Awang,

Ali

et al. 2024

Health Science Reports

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Background and AimsAlzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis.MethodsWe searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings.Results and ConclusionThe ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation.

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3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

Cited by 8 publications

References 13 publications

Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI

Evaluation of Transfer Learning Methods for Detecting Alzheimer’s Disease with Brain MRI

Secure Federated Learning for Neuroimaging

Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review

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