Welcome to my personal website. I am a Postdoctoral Researcher in the
Department of Electronic Engineering and a Researcher at the
Institute of Integrated Biotechnology at Sogang University, Seoul, Korea.
I received B.S. and Ph.D. degrees in Electronic Engineering from Sogang University, Seoul, Korea.
My research focuses on advancing medical imaging technologies, with a
particular emphasis on ultrasound imaging and the integration of
deep learning to enhance imaging quality and diagnostic precision.
I am always open to engaging in discussions on interesting research topics and exploring opportunities
for collaboration. If you're interested, please feel free to contact me via email.
Research Interests
Deep Learning Based Medical Image Analysis
Automatic measurement of clinical parameters using deep learning based segmentation and detection
Controllable computation algorithms for clinically reliable medical image analysis
Deep Learning Driven Ultrasound Imaging
Unsupervised and self-supervised learning for high-quality ultrasound image reconstruction
Deep learning based inverse problem solving for artifact reduction
Deep learning based quantitative imaging and perfusion imaging
Data-Efficient and Computation-Efficient Deep Learning
Physics-informed pseudo data generation for data-efficient deep learning
Unsupervised and self-supervised learning for medical image analysis
Model compression and acceleration using hardware accelerators
Hardware-aware neural architecture searching for medical applications
Education & Experience
Education
2020 - 2025
Ph.D. in Electronic Engineering
Sogang University, Seoul, Korea
Thesis: A Study on Unsupervised Deep Neural Networks for Medical Ultrasound Imaging
2015 - 2020
B.S. in Electronic Engineering
Sogang University, Seoul, Korea
Experience
2025 - Present
Postdoctoral Researcher
Department of Electronic Engineering
Sogang University, Seoul, Korea
2023 - Present
Researcher
Institute of Integrated Biotechnology
Sogang University, Seoul, Korea
2022 - 2023
Research Engineer
Edgecare Inc.
Seoul, Republic of Korea
2020 - 2025
Research Assistant
Department of Electronic Engineering
Sogang University, Seoul, Korea
Development of a deep learning model for automatic detection of narrowed intervertebral disc space sites in caudal thoracic and lumbar lateral X-ray images of dogs
Frontiers in Veterinary Science, 11, 1453765.
2023-JCR-IF: 2.6 | Top 12.9% in Veterinary Sciences
EdgeSRIE: A hybrid deep-learning framework for real-time speckle reduction and image enhancement in portable ultrasound systems
2025
Three more papers are currently under review.
Conference Presentations
2025
Cho, H., Lee, J., Kang, J., & Yoo, Y. EdgeSRIE: A hybrid deep-learning framework for real-time speckle reduction and image enhancement in portable ultrasound systems.
IEEE International Ultrasonics Symposium (IUS)
Oral Presentation
2025
Kim, E., Cho, H., & Yoo, Y. DeepStitch: A self-supervised deep neural network for seamless image reconstruction of robot-assisted ultrasound system.
IEEE International Ultrasonics Symposium (IUS)
2025
Lee, J., Cho, H., Park, S., & Yoo, Y. DeepTAI: Deep learning-based twinkling artifact imaging for microcalcification detection in breast ultrasound.
IEEE International Ultrasonics Symposium (IUS)
2025
Lee, E., Cho, H., Yoo, Y. A new compounding method using spatiotemporal features for twinkling artifact imaging.
IEEE International Ultrasonics Symposium (IUS)
2025
Yoo, J., Han, S., Park, S., Song, I., Cho, H., & Yoo, Y. A new vector Doppler technique for cost-effective hemodynamic monitoring.
IEEE International Ultrasonics Symposium (IUS)
2025
Cho, H., Yoo, Y. DeepSVD: A deep learning based singular value decomposition for high-quality ultrasound perfusion imaging.
The 16th International Conference on Ultrasound Engineering for Biomedical Applications (IC-UEBA)
2025
Lee, J., Cho, H., & Yoo, Y. A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems.
The 16th International Conference on Ultrasound Engineering for Biomedical Application (IC-UEBA)
2025
Lee, E., Cho, H., & Yoo, Y. Spatiotemporal singular value decomposition for ultrasound twinkling artifact imaging.
The 16th International Conference on Ultrasound Engineering for Biomedical Application (IC-UEBA)
2025
Kwon, Y., Cho, H., Yoo, Y. Self-supervised deconvolutional neural network for high-resolution ultrasound imaging: Preliminary results.
The 16th International Conference on Ultrasound Engineering for Biomedical Applications (IC-UEBA)
2025
Yoo, J., Han, S., Cho, H., & Yoo, Y. A new vector Doppler technique for cost-effective hemodynamic monitoring.
The 16th International Conference on Ultrasound Engineering for Biomedical Application (IC-UEBA)
2025
Kim, E., Cho, H., & Yoo, Y. DeepStitch: A self-supervised deep neural network for seamless image reconstruction of robot-assisted ultrasound system.
The 16th International Conference on Ultrasound Engineering for Biomedical Application (IC-UEBA)
2024
Lee, J., Cho, H., & Yoo, Y. Unsupervised Domain Adaptation Method for Deep Learning Based Speed-of-Sound Estimation.
The 64th Congress of the Korean Society of Medical & Biological Engineering (KOSOMBE)
2024
Kim, E., Cho, H., & Yoo, Y. Genetically Evolved Efficient Deep Neural Network for Portable Echocardiography Devices.
The 64th Congress of the Korean Society of Medical & Biological Engineering (KOSOMBE)
2024
Cho, H., Kang, J., & Yoo, Y. An Unsupervised Deep Neural Network for Reverberation Artifact Reduction in Medical Ultrasound.
IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)
Oral Presentation
2024
Cho, H., Kang, J., & Yoo, Y. An Unsupervised Deep Clutter Filter for High-Quality Ultrafast Perfusion Imaging.
IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)
Oral Presentation
2024
Lee, J., Cho, H., & Yoo, Y. Unsupervised Domain Adaptation Method for Deep Learning Based Speed-of-Sound Estimation.
IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS)
2024
Cho, H., & Yoo, Y. An Unsupervised Deep Neural Network for Reverberation Artifact Reduction.
The 16th Congress of the Asian Federation of Societies for Ultrasound in Medicine and Biology (AFSUMB)
Oral Presentation
2023
Cho, H., Kang, J., & Yoo, Y. An Unsupervised Deep Beamformer for High-Quality Ultrafast Ultrasound Imaging.
The 19th World Federation for Ultrasound in Medicine and Biology Congress (WFUMB)
Oral Presentation
2023
Cho, H., Kang, J., & Yoo, Y. Deep Coherence Learning: An Unsupervised Deep Learning Framework for High-Quality Plane Wave Imaging.
International Symposium on Integrated Medical Solutions (iSims)
2023
Cho, H., & Yoo, Y. A Self-Supervised Learning Framework for Artifact-Free High-Quality Plane Wave Imaging.
The 54th Annual Congress of Korean Society of Ultrasound in Medicine (KSUM)
Oral Presentation
2023
Cho, H., Park, S., Kang, J., & Yoo, Y. Deep Coherence Learning: an Unsupervised Deep Learning Framework for High-Quality Plane Wave Imaging.
IEEE International Ultrasonics Symposium (IUS)
Oral Presentation
2023
Cho, S., Cho, H., Jang, J., Song, I., & Yoo, Y. Automated Ultrasound Bladder Volume Measurement System Based on Lightweight Deep Learning Network on Point-of-Care (POC) Device.
IEEE International Ultrasonics Symposium (IUS)
2023
Kim, D., Cho, H., & Yoo, Y. Hardware-Accelerated Lightweight Deep Learning Model for Automatic Cardiac Parameter Measurement on Low-Resource Portable Devices.
IEEE International Ultrasonics Symposium (IUS)
2022
Cho, H., Jang, J., & Yoo, Y. Residual CNN based Angular Compounding for High-quality Plane Wave Imaging.
IEEE International Ultrasonics Symposium (IUS)
2022
Park, S., Cho, H., Kim Y., Song, I., Jang, J., Kim, J., & Yoo, Y. A Real-Time Color Doppler Ultrasound Imaging System on a Single System-on-Chip Solution with Single Instruction Multiple Data Optimization.
IEEE International Ultrasonics Symposium (IUS)
2022
Park, D., Cho, H., Song, I., Jang, J., Kim, J., & Yoo, Y. Automated Bladder Volume Measurement System Using a T-Shaped Cross-Array Ultrasound Probe.
IEEE International Ultrasonics Symposium (IUS)
2020
Cho, H., Kang, J., Chang, S., & Yoo, Y. CNN-Based Semantic Segmentation Networks for Multiple Fetal Biometric Measurements.
IEEE International Ultrasonics Symposium (IUS)
Invited Talks
2025
Cho, H. Unsupervised deep neural network for speckle reduction and image enhancement on portable ultrasound devices.
Medical AI Laboratory, MODULABS Inc., Seoul, Republic of Korea (12 Feb. 2025)
Invited Talk
2025
Cho, H. Unsupervised deep neural networks for high-quality ultrasound imaging.
Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea (17 Jan. 2025)
Invited Talk
2025
Cho, H. Advanced ultrasound imaging with unsupervised deep neural networks.
Medical AI Laboratory, MODULABS Inc., Seoul, Republic of Korea (08 Jan. 2025)
Invited Talk
2024
Cho, H. Recent advances in deep learning-driven medical imaging.
The Catholic University of Korea, Bucheon, Republic of Korea (07 May 2024)
Invited Talk
2024
Cho, H. Medical imaging with deep learning.
College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea (30 Jan. 2024)
Invited Talk
2024
Cho, H. Automatic design of neural networks for medical image segmentation.
Medical AI Laboratory, MODULABS Inc., Seoul, Republic of Korea (24 Jan. 2024)
Invited Talk
2023
Cho, H. Specialized computer architectures for deep neural networks.
Department of Electronic Engineering, Sogang University, Seoul, Republic of Korea (12 Dec. 2023)
Invited Talk
2023
Cho, H. Deep learning-based body composition analysis.
Medical AI Laboratory, MODULABS Inc., Seoul, Republic of Korea (28 Jun. 2023)
Invited Talk
Awards & Honors
2024
Student Travel Grant Award
IEEE UFFC-JS 2024
2024
Best Paper Award - 3rd Place
Sogang-Samsung Paper Award, Samsung Electronics
2023
The Young Investigator Prize
19th WFUMB Congress
2023
Young Investigator Award - Gold
54th Annual Congress of KSUM
2023
Best Poster Award
International Symposium on Integrated Medical Solutions (iSims)
2023
Best Paper Award - 1st Place
Department of Electronic Engineering, Sogang University