Pinned Repositories
DNN_TL4fMRI
Official python code for "Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification"
SGNN_TL_SNUH
Codes for transfer learning of SGNN trained on ABCD data to SNUH data
RSNA-2023-Abdominal-Trauma-Detection
RSNA 2023 Kaggle competition
ImagingGenetics
Imaging genetics data for my research
dnnwsp
Deep neural network (DNN) with weight sparsity control (i.e., L1-norm regularization) improved the classification performance using whole-brain resting-state functional connectivity patterns of schizophrenia patient and healthy groups.
Medical_AI_tutorial
Practice page for AI tutorial (for me and anyone else who are not familiar with AI)
SGNN
Official PyTorch code for "General Psychopathology Factor (p-factor) Prediction Using Resting-State Functional Connectivity and a Scanner-Generalization Neural Network"
SGNN_TL_SNUH-1
abcd-hcp-pipeline
bids application for processing functional MRI data, robust to scanner, acquisition and age variability.
dnnwsp
Deep neural network (DNN) with weight sparsity control (i.e., L1-norm regularization) improved the classification performance using whole-brain resting-state functional connectivity patterns of schizophrenia patient and healthy groups.
JD-Hwang's Repositories
JD-Hwang/SGNN_TL_SNUH-1
JD-Hwang/Medical_AI_tutorial
Practice page for AI tutorial (for me and anyone else who are not familiar with AI)
JD-Hwang/SGNN_TL_SNUH
Codes for transfer learning of SGNN trained on ABCD data to SNUH data
JD-Hwang/SGNN
Official PyTorch code for "General Psychopathology Factor (p-factor) Prediction Using Resting-State Functional Connectivity and a Scanner-Generalization Neural Network"
JD-Hwang/ImagingGenetics
Imaging genetics data for my research
JD-Hwang/RSNA-2023-Abdominal-Trauma-Detection
RSNA 2023 Kaggle competition
JD-Hwang/DNN_TL4fMRI
Official python code for "Autoencoder and restricted Boltzmann machine for transfer learning in functional magnetic resonance imaging task classification"
JD-Hwang/dnnwsp
Deep neural network (DNN) with weight sparsity control (i.e., L1-norm regularization) improved the classification performance using whole-brain resting-state functional connectivity patterns of schizophrenia patient and healthy groups.