Eunjung Lee

Professor

Yonsei University

South Korea

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Professor Eunjung Lee is a faculty member at Yonsei University in South Korea. Her research focuses on quantum transport computation, radiotranscriptomics, and artificial intelligence applications in medical diagnostics, particularly related to thyroid nodules and papillary thyroid carcinoma. She has contributed to significant advancements in non-invasive risk stratification and finite element approximations in complex materials, as evidenced by her recent publications.

Articles (18)

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2026

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2025

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2021

Collaborators (8)

Mikyoung LIM

Prof.

Korea Advanced Institute of Science and Technology

SOUTH KOREA

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Hanpyo Hong

Research professor

Yonsei University College of Medicine

SOUTH KOREA

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Jin Young Kwak

Yonsei University College of Medicine

SOUTH KOREA

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Jung Hyun Yoon

Yonsei University

SOUTH KOREA

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Hye Sun Lee

Yonsei University College of Medicine

SOUTH KOREA

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Vivian Park

Seoul National University College of Medicine

SOUTH KOREA

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Eun Cho

Assistant Professor

Gyeongsang National University College of Medicine

SOUTH KOREA

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Soo Myoung Shin

Clinical Assiatant Professor

Seoul National University Hospital

SOUTH KOREA

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Eunjung Lee

Research Interests

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Articles (18)

Theoretical analysis and high-performance implementation of low-rank approximations in NEGF-based quantum transport

A novel least squares approach generating approximations orthogonal to the null space of the operator

Radiotranscriptomics in Papillary Thyroid Carcinoma Complement Current Non-Invasive Risk Stratification System

A new approach for efficient nonequilibrium quantum transport computation in electroluminescent quantum dots

Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning

Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance

Enforcing interface field continuity to advance finite element approximations in heterogeneous materials

Corrigendum: Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance(Front. Endocrinol., (2024), 15, (1372397), 10.3389/fendo.2024.1372397)

Radiomics Analysis of Gray-Scale Ultrasonographic Images of Papillary Thyroid Carcinoma > 1 cm: Potential Biomarker for the Prediction of Lymph Node Metastasis

An L2-finite element approximation of the solution to div/curl-systems with nonempty null space

Dual least‐squares finite element method with stabilization

Artificial Intelligence Computer-Assisted Diagnosis for Thyroid Nodules: Comparison of Diagnostic Performance Using Original and Mobile Ultrasonography Images

Dual System Least-Squares Finite Element Method for a Hyperbolic Problem

Stability analysis of the implicit finite difference schemes for nonlinear Schrödinger equation

Convolutional Neural Network to Stratify the Malignancy Risk of Thyroid Nodules: Diagnostic Performance Compared with the American College of Radiology Thyroid Imaging Reporting and Data System Implemented by Experienced Radiologists

Numerical solution to the interface problem in a general domain using Moser’s deformation method

A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound

Author Correction: A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound (Scientific Reports, (2021), 11, 1, (20448), 10.1038/s41598-021-99983-6)

Collaborators (8)

Mikyoung LIM

Hanpyo Hong

Jin Young Kwak

Jung Hyun Yoon

Hye Sun Lee

Vivian Park

Eun Cho

Soo Myoung Shin

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