Search Results for author:
Found 14 papers, 4 papers with code
Neural Responses to Affective Sentences Reveal Signatures of Depression
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected.
A Feasibility Study of Task-Based fMRI at 0.55 T
0. 55T MRI offers advantages compared to conventional field strengths, including reduced susceptibility artifacts and better compatibility with simultaneous EEG recordings.
Deep Learning Characterizes Depression and Suicidal Ideation from Eye Movements
We were able to identify depression and suicidal ideation with an area under the receiver operating curve (AUC) of 0. 793 (95% CI: 0. 765-0. 819) against healthy controls, and suicidality specifically with 0. 826 AUC (95% CI: 0. 797-0. 852).
Generalizable Representation Learning for fMRI-based Neurological Disorder Identification
We overcome this limitation by introducing a novel representation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction
To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning.
Neuro-GPT: Towards A Foundation Model for EEG
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model.
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data.
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications.
Semi-supervised Learning using Robust Loss
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks.
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies
While pointwise analysis methods are common in anatomical studies such as cortical thickness analysis and voxel- and tensor-based morphometry and its variants, such a method is lacking for rs-fMRI and could improve the utility of rs-fMRI for group studies.
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Using estimated quantiles to compute mean and variance under the Gaussian assumption, we compute reconstruction probability as a principled approach to outlier or anomaly detection.
Robust Variational Autoencoder for Tabular Data with Beta Divergence
The source of outliers in training data include the data collection process itself (random noise) or a malicious attacker (data poisoning) who may target to degrade the performance of the machine learning model.
Robust Variational Autoencoder
Machine learning methods often need a large amount of labeled training data.
