kyulee-jeon Goto Github PK

bootcamp_kr

sharing bootcamp contents

brain-gbmmeta_de

Deep Ensembles code

cyclops

Cyclops (Cyclic coordinate descent for logistic, Poisson and survival analysis) is an R package for performing large scale regularized regressions.

dare-covid-1

dcmqi

dcmqi (DICOM for Quantitative Imaging) is a free, open source library that can help with the conversion between imaging research formats and the standard DICOM representation for image analysis results

dicom-header-extractor

dicom-header-extractor_java

dicom2omop

This project looks at creating a controlled vocabulary for DICOM Pt 6 Data Dictionary with a focus on CS code strings.

ecg_python

ecgsurvnet

education-computervision-deeplearning

gbm-differentiation-uncertainty

Jupyter notebook files for training and testing deep ensembles for differentiating glioblastoma (GBM) from solitary brain metastasis (SBM). This model provides uncertainty and interpretability etimation.

highdicom

High-level DICOM abstractions for the Python programming language

howoften

[under development] Large-scale incidence characterization

imagewg

Repository for medical image working group

kyulee-jeon

Config files for my GitHub profile.

lidc-idri

ltsf-linear

This is the official implementation for AAAI-23 paper "Are Transformers Effective for Time Series Forecasting?"

machine-learning

Repository for activities of the machine learning committee

medicalzoopytorch

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

medmnist

18 MNIST-like Datasets for 2D and 3D Biomedical Image Classification: try "pip install medmnist"!

medmnist_experiments

Codebase for reproducible benchmarking experiments in MedMNIST v2

medsam

Segment Anything in Medical Images

mlcube_examples

MLCube® examples

monai

MONAI Tutorials

monai_bootcamp

multi_diagnosis_classification

ohdsikoreatutorials

The repository of the materials for the OHDSI Tutorials in Korea

phenophebstemicharacterization

This comprehensive study aims to understand STEMI patients’ characteristics and identify the incidence rates across multiple real-world data datasets.

public