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Found 7 papers, 4 papers with code
GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity.
Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity
This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity.
Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs
Many neurological diseases are characterized by gradual deterioration of brain structure and function.
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain.
Representation Learning with Statistical Independence to Mitigate Bias
Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years.
Bias-Resilient Neural Network
We apply our method to a synthetic, a medical diagnosis, and a gender classification (Gender Shades) dataset.
Confounder-Aware Visualization of ConvNets
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images.
