Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Couvy‐Duchesne, Baptiste; Faouzi, Johann; Martin, Benoit; Thibeau–Sutre, Elina; Wild, Adam; Ansart, Manon; Durrleman, Stanley; Dormont, Didier; Burgos, Ninon; Colliot, Olivier

doi:10.3389/fpsyt.2020.593336

Cited by 30 publications

(29 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, DL leverages the gradient descent algorithm to automatically search for a series of nonlinear transformations for feature extraction ( 61 ), which is more efficient and has the ability to obtain the optimal representation from the least preprocessed raw input data for a specific task. Additionally, in combination with ensemble learning, DL-based brain age estimation achieved superior predictive performance ( 62 , 63 ). Our results also demonstrate that with a well-trained brain age model, DL could improve predictive accuracy and decrease prediction bias.…”

Section: Discussionmentioning

confidence: 99%

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Kuo

Tai²,

Lee

et al. 2021

Front. Psychiatry

View full text Add to dashboard Cite

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R2 = 0.88; support vector regression, MAE = 4.42 years, R2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Kuo

Tai²,

Lee

et al. 2021

Front. Psychiatry

View full text Add to dashboard Cite

show abstract

“…Both the Inception and the ResNet models were trained using the cross entropy loss weighted according to the proportion of images per class, the Adam optimizer with a learning rate of 1e-4 and the batch size was set to 2. These two models have been used in (Couvy-Duchesne et al, 2020) to predict brain age from 3D T1w MRI. For that specific task, they achieved a higher performance than the 5-layer CNN mentioned above.…”

Section: Network Architecturementioning

confidence: 99%

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Bottani

Burgos

Maire³

et al. 2022

Medical Image Analysis

Self Cite

View full text Add to dashboard Cite

Didier Dormont, Olivier Colliot, APPRIMAGE Study Group• We propose a framework for the automatic QC of 3D T1w brain MRI for a clinical data warehouse.• We manually labeled 5500 images to train/test different convolutional neural networks.• The automatic approach can identify images which are not proper T1w brain MRIs (e.g. truncated images).• It is able to identify acquisitions for which gadolinium was injected.• It can also accurately identify low quality images.

show abstract

“…Ensembles have been shown to predict more accurately and reduce model biases (Dietterich, 2000), also in the domain of BA prediction (Couvy-Duchesne et al, 2020;Dinsdale et al, 2021;Jonsson et al, 2019;Levakov et al, 2020;Peng et al, 2021). The individual predictions of the base models were used to train and evaluate a linear head model of the respective subensemble, leading to a weighted prediction of the whole ensemble.…”

Section: Model Architecturementioning

confidence: 99%

Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

Beyer

Lapuschkin

et al. 2021

Preprint

View full text Add to dashboard Cite

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.38-5.07 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as lesions, iron accumulations and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.

show abstract

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Cited by 30 publications

References 55 publications

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse

Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

Contact Info

Product

Resources

About