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Found 9 papers, 0 papers with code
Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study
This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD.
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
MEDL separately quantifies cluster-invariant fixed effects (FE) and cluster-specific random effects (RE) through the introduction of: 1) a cluster adversary which encourages the learning of cluster-invariant FE, 2) a Bayesian neural network which quantifies the RE, and a mixing function combining the FE an RE into a mixed-effect prediction.
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence.
DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models
We present two novel hyperparameter optimization strategies for optimization of deep learning models with a modular architecture constructed of multiple subnetworks.
Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data
We propose a general-purpose framework for Adversarially-Regularized Mixed Effects Deep learning (ARMED) models through non-intrusive additions to existing neural networks: 1) an adversarial classifier constraining the original model to learn only cluster-invariant features, 2) a random effects subnetwork capturing cluster-specific features, and 3) an approach to apply random effects to clusters unseen during training.
Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI
We propose a new machine learning based measure of functional connectivity which efficiently captures linear and nonlinear aspects.
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder
Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions associated with deficits in social and sensory processing in ASD.
Prediction of individual progression rate in Parkinson's disease using clinical measures and biomechanical measures of gait and postural stability
The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stability to predict an individual's PD progression over two years.
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning
This method is tested on a challenging task of predicting antidepressant treatment response from pre-treatment task-based fMRI and demonstrates a 26% improvement in performance in predicting response using augmented images.
