Heejong Kim

Instructor

About

I am an instructor in Mert Sabuncu's lab at Cornell and Weill Cornell Medicine. I earned my Ph.D. at New York University under guidance of Guido Gerig. My research interest lies in discovering computational techniques and their application to biomedical images.

Recent Updates

April 2024: Thrilled to announce that my NIH K25 career development grant has been awarded! I am excited to explore new opportunities and advance research in prostate cancer.
April 2024: Our review paper on the interpretability of machine learning model for medical imaging (MLMI) led by Alan Wang is published in IEEE Access (Paper).
Oct 2023: Our paper presenting a new method for adjusting for variable confounding associations led by Minh Nguyen was accepted by WACV 2023 (Paper).
Oct 2023: I was invited to give a talk at Asilomar Conference on Signals, Systems, and Computers.
Sep 2023: I was invited to give a talk at Annual Symposium for Evolving Therapies and Drug Development in Oncology.

Publications

Robust Learning via Conditional Prevalence Adjustment

M Nguyen, AQ Wang, H Kim, MR Sabuncu

Pre-print WACV accepted, 2023

[ Paper | Code ]

A Framework for Interpretability in Machine Learning for Medical Imaging

AQ Wang, BK Karaman, H Kim, J Rosenthal, R Saluja, SI Young, MR Sabuncu

Pre-print, 2023.

[ Paper ]

Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging

H Kim, VI Butoi, AV Dalca, DJA Margolis, MR Sabuncu

MICCAI Workshop MedAGI: Foundation Models for General Medical AI, 2023.

[ Paper ]

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges

H Kim, SW Kang, J Kim, H Nagar, MR Sabuncu, DJA Margolis, CK Kim

European Journal of Radiology, 2023

[ Paper ]

Learning to Compare Longitudinal Images

H Kim, MR Sabuncu

MIDL: Medical Imaging with Deep Learning, 2023

[ Paper | Code | Webpage | Oral presentation ]

Pulse Sequence Dependence of a Simple and Interpretable Deep Learning Method for Detection of Clinically Significant Prostate Cancer Using Multiparametric MRI

H Kim, DJA Margolis, H Nagar, MR Sabuncu

Academic Radiology, 2022

[ Paper | Code ]

Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

M Ren^∗, H Kim^∗, N Dey, G Gerig

MICCAI: Medical Image Computing and Computer Assisted Intervention, 2021.

[ Paper | Code | Webpage | Oral presentation |^∗Equal contribution ]

Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data

A Elaldi, N Dey, H Kim, G Gerig

IPMI: Information Processing in Medical Imaging, 2021.

[ Paper | Code ]

Microparticle-based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning

S Shah, CN Yu, M Zheng, H Kim, MS Eggleston

ACS Nano, 2021.

[ Paper ]

Hierarchical Geodesic Modeling on the Diffusion Orientation Distribution Function for Longitudinal DW-MRI Analysis

H Kim, S Hong, M Styner, J Piven, K Botteron, G Gerig

MICCAI: Medical Image Computing and Computer Assisted Intervention, 2020.

[ Paper ]

A framework to construct a longitudinal dw-mri infant atlas based on mixed effects modeling of dodf coefficients

H Kim, M Styner, J Piven, G Gerig

MICCAI CDMRI: Computational Diffusion MRI, 2020.

[ Paper | Oral presentation | Best oral presentation award ]

Longitudinal structural connectivity in the developing brain with projective non-negative matrix factorization

H Kim, J Piven, G Gerig

SPIE Medical Imaging, 2019.

[ Paper | Oral presentation | Best paper finalist ]

Using individualized brain network for analyzing structural covariance of the cerebral cortex in alzheimer's patients

HJ Kim, JH Shin, CE Han, HJ Kim, DL Na, SW Seo, JK Seong, and the ADNI

Frontiers in neuroscience, 2016.

[ Paper ]

Tract-specific correlates of neuropsychological deficits in patients with subcortical vascular cognitive impairment

NY Jung, CE Han, HJ Kim, SW Yoo, HJ Kim, EJ Kim, DL Na, SN Lockhart, WJ Jagust, JK Seong, SW Seo

Journal of Alzheimer's Disease, 2016.

[ Paper ]