Eung Yeop Kim

Professor

Samsung Medical Center

South Korea

Research Interests

Explore related searches

Professors in South Korea Professors in Medical Imaging Medical Imaging professors in South Korea

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Eung Yeop Kim is a Professor at Samsung Medical Center in South Korea. His research areas focus on advanced imaging techniques in neurodegenerative diseases, including the differentiation of multiple system atrophy and Parkinson's disease. He has published extensively on the application of deep learning in radiology, particularly in identifying abnormalities in brain imaging and improving diagnostic accuracy.

Articles (19)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Collaborators (16)

Chul Hyoung Lyoo

Professor

Gangnam Severance Hospital

SOUTH KOREA

Eung Yeop Kim

Research Interests

Explore related searches

Contact this professor

About

Articles (19)

The Inferior Cerebellar Peduncle Sign: A Novel Imaging Marker for Differentiating Multiple System Atrophy Cerebellar Type from Spinocerebellar Ataxia

Susceptibility map-weighted MRI can distinguish tremor-dominant Parkinson’s disease from essential tremor

Effect of Deep Learning-Based Artificial Intelligence on Radiologists’ Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging

Comparative Performance of Susceptibility Map-Weighted MRI According to the Acquisition Planes in the Diagnosis of Neurodegenerative Parkinsonism

Development and Multicenter, Multiprotocol Validation of Neural Network for Aberrant Right Subclavian Artery Detection

Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application

SPINNED: Simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data

Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds

Detection of Cerebral Microbleeds in MR Images Using a Single‐Stage Triplanar Ensemble Detection Network (TPE‐Det)

Nigrosome 1 visibility and its association with nigrostriatal dopaminergic loss in Parkinson's disease

Diagnostic performance of a high-spatial-resolution voxelwise analysis of neuromelanin-sensitive imaging in early-stage idiopathic Parkinson’s disease

Outcome of epidural blood patch for imaging-negative spontaneous intracranial hypotension

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches

A Deep Learning-Based Automatic Collateral Assessment in Patients with Acute Ischemic Stroke

Correlation Between Neuromelanin-Sensitive MRI and 18F-FP-CIT PET in Early-Stage Parkinson’s Disease: Utility of a Voxel-Wise Analysis by Using High-Spatial-Resolution MRI

Increasing discrepancy of MR imaging and CSF study in patients with leptomeningeal seeding from lung adenocarcinoma after targeted therapy using a tyrosine kinase inhibitor

A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism

Continuous Conversion of CT Kernel Using Switchable CycleGAN With AdaIN

Early‐stage Parkinson's disease: Abnormal nigrosome 1 and 2 revealed by a voxelwise analysis of neuromelanin‐sensitive MRI

Collaborators (16)

Chul Hyoung Lyoo

MOHAMMED AL-MASNI

Sung-Hong Park

Young Hee Sung

Jongho Lee

Sung Tae Kim

Dong‐Hyun Kim

Sun Ju Chung

Mi Ji Lee

Hyun Jin Han

Soohyun Cho

Yongsik Sim

Jihoon Cha

Yikyung Kim

Hyung-Jin Kim

Beomseok Sohn

Make your next move.