Hyunna Lee

发表

Deep Learning in Medical Imaging: General Overview

June-Goo Lee, Sanghoon Jun, Younghoon Cho, 2017, Korean journal of radiology.

Construction and Validation of Brain MRI Templates from a Korean Normal Elderly Population

Ji Won Han, Hyunna Lee, B. I. Yoo, 2015, Psychiatry investigation.

Comparison of three image comparison methods for the visual assessment of the image fidelity of compressed computed tomography images.

Yeong-Gil Shin, Jinwook Seo, Seongjin Park, 2011, Medical physics.

Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms

Hyunna Lee, Namkug Kim, D. Kang, 2019, Korean journal of radiology.

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer

Jeong Hwan Park, Hyunna Lee, Namkug Kim, 2020, Cancer research and treatment : official journal of Korean Cancer Association.

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy

Hyunna Lee, Namkug Kim, K. Moon, 2020, Modern Pathology.

Does the amount of tagged stool and fluid significantly affect the radiation exposure in low-dose CT colonography performed with an automatic exposure control?

Hyunna Lee, Kil Joong Kim, S. Park, 2011, European Radiology.

A Case of Secondary Organizing Pneumonia Associated with Endobronchial Actinomycosis

J. Yim, Hyunna Lee, Seungmin Lee, 2007 .

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer

Jeong Hwan Park, Hyunna Lee, Namkug Kim, 2020, Cancer research and treatment : official journal of Korean Cancer Association.

Stability of Radiomic Features in Fast Scanned FLAIR for Acute Ischemic Stroke Patients: A Comparison With Conventional FLAIR

Hyunna Lee, S. Park, J. E. Park, 2021 .

Machine Learning Approach to Identify Stroke Within 4.5 Hours

Eun-Jae Lee, Sungwon Ham, Namkug Kim, 2020, Stroke.

Feasibility study on low-dosage digital tomosynthesis (DTS) using a multislit collimation technique

Hyunna Lee, D. Jeon, Gwangmook Kim, 2018 .

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer

Hyunna Lee, Namkug Kim, S. Y. Park, 2022, Cancer research and treatment.

Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume

Hyunna Lee, Ki Woong Kim, Hyunna Lee, 2019, Journal of psychiatry & neuroscience : JPN.

Stability of MRI radiomic features according to various imaging parameters in fast scanned T2-FLAIR for acute ischemic stroke patients

Hyunna Lee, S. Park, J. E. Park, 2021, Scientific Reports.

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Hyunna Lee, Namkug Kim, Young-Gon Kim, 2019, Scientific Reports.

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

Hyunna Lee, Namkug Kim, S. Park, 2020, European Radiology.

Artificial Intelligence in Health Care: Current Applications and Issues

Seungwook Paek, Jong Chul Ye, Namkug Kim, 2020, Journal of Korean medical science.

Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma

Hyunna Lee, Namkug Kim, H. Kim, 2019, Scientific Reports.

The comparison of the benefits obtained from platinum-containing chemotherapy between triple-negative and non-triple-negative metastatic breast cancer.

Hyunna Lee, S. Im, D. Oh, 2010 .

Fully automated segmentation on brain ischemic and white matter hyperintensities lesions using semantic segmentation networks with squeeze-and-excitation blocks in MRI

Namkug Kim, Hyunna Lee, Ilsang Woo, 2020 .

EBNA1 binding and epigenetic regulation of gastrokine tumor suppressor genes in gastric carcinoma cells

Hyunna Lee, I. Tempera, P. Lieberman, 2014, Virology Journal.

Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?

Hyunna Lee, C. Suh, J. Roh, 2023, Scientific reports.

Association of the Irregular 3-Dimensional Shape of White Matter Hyperintensities with Cognitive Function

Grace Eun Kim, Hyunna Lee, J. H. Kim, 2021, European Neurology.

A model-based radiography restoration method based on simple scatter-degradation scheme for improving image visibility

Chulkyu Park, Sungho Kang, Sung Yul Park, 2018 .

Interactive registration between supine and prone scans in computed tomography colonography using band-height images

Yeong-Gil Shin, Ho Chul Kang, Jeongjin Lee, 2017, Comput. Biol. Medicine.

A Pilot Study on Automatic Diagnosis of Cancer Cells Metastasis in Frozen Section Using Convolutional Neural Network

Hyunna Lee, S. Y. Park, G. Gong, 2020 .

Reproducibility of radiomic features in SENSE and compressed SENSE: impact of acceleration factors

Hyunna Lee, S. Park, J. E. Park, 2021, European Radiology.

Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches

Namkug Kim, Da-in Eun, Ryoungwoo Jang, 2020, Scientific Reports.

A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis

Chulkyu Park, Sungho Kang, Younghwan Lim, 2018, Optics and Lasers in Engineering.

Fully Automated and Real-Time Volumetric Measurement of Infarct Core and Penumbra in Diffusion- and Perfusion-Weighted MRI of Patients with Hyper-Acute Stroke

Namkug Kim, Hyunna Lee, Kyesam Jung, 2019, Journal of Digital Imaging.

Intensity-vesselness Gaussian mixture model (IVGMM) for 2D + t segmentation of coronary arteries for X-ray angiography image sequences.

Hackjoon Shim, Hyuk-Jae Chang, Hyunna Lee, 2015, Journal of X-ray science and technology.

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Namkug Kim, Beomhee Park, Yongwon Cho, 2019, Scientific Reports.

A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA

Dongjin Han, Youngtaek Hong, Hackjoon Shim, 2014, Comput. Methods Programs Biomed..

Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma

Namkug Kim, Jihye Yun, Sungwon Ham, 2019, Scientific Reports.

Fully Automated and Real-Time Volumetric Measurement of Infarct Core and Penumbra in Diffusion- and Perfusion-Weighted MRI of Patients with Hyper-Acute Stroke

Hyunna Lee, Namkug Kim, D. Kang, 2019, Journal of Digital Imaging.