Hyeongsub Kim

Greetings! 

My name is Hyeongsub Kim, and I am currently working as an AI Research Scientist at LG CNS in South Korea. I am actively engaged in a project with Daehan Steel, a leading steel manufacturing company in South Korea, focused on deep learning-based steel scrap classification. My specific interest lies in sensor fusion algorithms, and I am driven to enhance the performance of AI models through this expertise. 

My career has predominantly centered around the medical domain. I have undertaken projects in artificial intelligence-based medical image analysis, particularly in pathology images, as well as bio-signal analysis. These experiences have broadened my understanding of various forms of data analysis. Building on my past endeavors, I am enthusiastic about exploring artificial intelligence research based on multi-modal data. Furthermore, I aspire to apply my skills to fields beyond the medical domain, contributing to making a positive impact on the world.

News

[Oct 2023] Paper on early prediction of respiratory failure in the neonatal intensive care unit using electronic health records has been accepted to BMC pediatrics. 

[Sep 2023] I joined LG CNS Vision AI Engineering Team as an AI research scientist.

[May 2023] My first bio-signal paper has been submitted to BMC pediatrics.

[Oct 2022] I joined VUNO Bio-signal Team as a research scientist.

[Sep 2022] Paper on medical image learning with limited and noisy data has been accepted to MICCAI 2022 workshop as a poster presentation. 

[Nov 2021] Paper on compressed domain segmentation has been accepted to Scientific Report.

[Aug 2021] I joined VUNO Digital Oncology Team as a research scientist.

Publications

Early Prediction of Need for Invasive Mechanical Ventilation in the Neonatal Intensive Care Unit using Artificial Intelligence and Electronic Health Records: A Clinical Study

Young A Kim†, Hyeongsub Kim†, Jaewoo Choi, Kyungjae Cho, Dongjoon Yoo, Yeha Lee, Su Jeong Park, Mun Hui Jeaong, Seong Hee Jeong, Kyung Hee Park, Shin-Yun Byun, Taehwa Kim, Sung-Ho Ahn, Woo Hyun Cho, Narae Lee.

In BMC Pediatrics 23.1 (2023): 525.

[Paper]

Deep learning-based computed tomographic image super-resolution via wavelet embedding

Hyeongsub Kim, Haenghwa Lee, Donghoon Lee.

In Radiation Physics and Chemistry 205 (2023): 110718.

[Paper]

Abstraction in Pixel-wise Noisy Annotation Can Guide Attention to Improve Prostate Cancer Assessment 

Hyeongsub Kim, Seo Taek Kong, Hongseok Lee, Kyungdoc kim, Kyu-Hwan Jung.

In MICCAI workshop, 2022 poster presentation.

[Paper] [Poster] 

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain

Hyeongsub Kim, Hongjoon Yoon,  Nishant Thakur, Gyoyeon Hwang, Eun Jung Lee, Chulhong Kim, Yosep Chong.

In Scientific Report 11 (1), 22520.

[Paper] 

GPU-accelerated 3D volumetric X-ray-induced acoustic computed tomography

Donghyun Lee,  Eun-Yeong Park, Seongwook Choi, Hyeongsub Kim, Jung-joon Min, Changho Lee, Chulhong Kim.

In Biomedical optics express 11 (2), 752-761.

[Paper] 

Education

• Mar 2018 - Feb 2021

Pohang University of Science and Technology (POSTECH), South Korea

M.S. in i-bio

• Mar 2009 - Feb 2018

Yonsei University, South Korea

B.S. in Radiological Science, Electrical Engineering