Chunsu Park

Chunsu Park

박춘수 | Ph.D. Student

University of Illinois Urbana-Champaign (UIUC)

I am a first-year Ph.D. student in Bioengineering at the University of Illinois Urbana-Champaign, honored to be under the supervision of Prof. Mark Anastasio and Prof. Hua Li. I received my Master’s degree in Information Convergence Engineering from Pusan National University, where I was fortunate enough to be advised by Prof. MinWoo Kim. I am also specially thankful to Prof. Chankue Park for advising me during our invaluable collaborative work.

My research interests lie in the intersection field of Imaging Science and Information Theory, especially their applications within Medical Imaging.

Interests
  • Image Science
  • Information Theory
  • Medical Imaging
  • Task-informed Imaging and Assessment
  • Generalization
  • Explainable AI
Education

  • University of Illinois Urbana-Champaign 2025 - present

    Ph.D in Bioengineering  

  • Pusan National University 2021 - 2023

    M.S in Information Convergence Engineering  

  • Jeonbuk National University 2010 - 2017

    B.S in Industrial and Information Systems Engineering 

Work Experience

  • Full-Time Research Fellow, AMILAB, Medical Research Institute, Pusan National University 2023 - 2025

    Insurance Product and System Manager, M Financial Service 2018 - 2021

Selected Publications

(† : co-first author , *: corresponding author)
CAPTURE-GAN: Conditional Attribute Preservation through Unveiling Realistic GAN for artifact removal in dual-energy CT imaging
CAPTURE-GAN: Conditional Attribute Preservation through Unveiling Realistic GAN for artifact removal in dual-energy CT imaging
Medical Image Computing and Computer Assisted Intervention (MICCAI 2024 (Acceptance: 857/2879 ≈ 29.8%)) October 2024
An innovative framework that enhances DECT imaging by effectively removing artifacts while preserving critical edema patterns using a classifier and masking within a generative adversarial network.
PDF Code
Convolutional Implicit Neural Representation of pathology whole-slide images
Convolutional Implicit Neural Representation of pathology whole-slide images
Medical Image Computing and Computer Assisted Intervention (MICCAI 2024 (Acceptance: 857/2879 ≈ 29.8%)) October 2024
This study explored the application of implicit neural representations (INRs) to enhance digital histopathological imaging.
PDF Code Project
Research Framework
W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT
Computerized Medical Imaging and Graphics (IF:5.4, JCR 2023 < 9%) July 2024
A new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations.
Code DOI
Research Framework
Prediction of bone mineral density in CT using deep learning with explainability
Frontiers in Physiology (IF:3.2, Q2) January 2023
A deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography.
Code Page DOI
Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists
Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists
European Journal of Radiology (IF:3.2, Q1) May 2022
Comparing the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT)
Page DOI

Contact