Kyungsu Lee

Assistant Professor

Jeju National University

South Korea

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About

Dr. Kyungsu Lee is an Assistant Professor at Jeonbuk National University, South Korea. His research focuses on innovative applications of machine learning and deep learning in medical imaging and localization, as evidenced by his recent articles on smartphone-based indoor localization, skull-universal acoustic holograms for neuromodulation, and intelligent bladder volume monitoring. Additionally, he explores advanced techniques in remote sensing imagery and automated diagnosis of skin diseases.

Articles (12)

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2025

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2025

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2024

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2024

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2023

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2022

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2022

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2022

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2021

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2021

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2021

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2021

Collaborators (6)

Haeyun Lee

Assistant Professor

SOUTH KOREA

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Jae Youn Hwang

Assistant Professor

Daegu Gyeongbuk Institute of Science and Technology

SOUTH KOREA

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Hyun-Lim Yang

Research Assistant Professor

Seoul National University Hospital

SOUTH KOREA

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Jun-Young Kim

Catholic University of Korea School of Medicine

SOUTH KOREA

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Inn-Chul Nam

The Catholic University of Korea

SOUTH KOREA

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Hyung-Chul Lee

Seoul National University

SOUTH KOREA

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Kyungsu Lee

Research Interests

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Articles (12)

SoN: Selective Optimal Network for smartphone-based indoor localization in real-time

Machine Learning-Enhanced Skull-Universal Acoustic Hologram for Efficient Transcranial Ultrasound Neuromodulation Across Varied Rodent Skulls

Intelligent Bladder Volume Monitoring for Wearable Ultrasound Devices: Enhancing Accuracy Through Deep Learning-Based Coarse-to-Fine Shape Estimation

Fine-Grained Binary Object Segmentation in Remote Sensing Imagery via Path-Selective Test-Time Adaptation

Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases

Deep‐learning model associating lateral cervical radiographic features with <scp>Cormack–Lehane</scp> grade 3 or 4 glottic view

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image

Boundary-Oriented Binary Building Segmentation Model With Two Scheme Learning for Aerial Images

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear

Intelligent smartphone-based multimode imaging otoscope for the mobile diagnosis of otitis media

Domain Adaptive Transfer Attack-Based Segmentation Networks for Building Extraction From Aerial Images

Collaborators (6)

Haeyun Lee

Jae Youn Hwang

Hyun-Lim Yang

Jun-Young Kim

Inn-Chul Nam

Hyung-Chul Lee

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