5 citations · 10 across the 13 of their papers we have counts for
13 papers
Unifying Active Learning and Semi-Supervised Learning for Medical Image Segmentation
Bahram Jafrasteh, Cheng Wan, Heejong Kim +2
In practical settings, medical image segmentation models are often developed with limited annotated data rather than fully labeled datasets. Training frequently begins in ultra-low…
MAdam: Metric-Aware Multi-Objective Adam
Fengbei Liu, Rachit Saluja, Sunwoo Kwak +5
Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost univers…
Single-Subject Multi-View MRI Super-Resolution via Implicit Neural Representations
Heejong Kim, Abhishek Thanki, Roel van Herten +2
Clinical MRI frequently acquires anisotropic volumes with high in-plane resolution and low through-plane resolution to reduce acquisition time. Multiple orientations are therefore…
AI-Based Detection of Temporal Changes in MR-Linac Images Acquired During Routine Prostate Radiotherapy
Seungbin Park, Peilin Wang, Ryan Pennell +6
Purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy and explore the broader potential of MRL…
Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models
Heejong Kim, Leo Milecki, Mina C Moghadam +5
Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as…
Adapting to Shifting Correlations with Unlabeled Data Calibration
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Distribution shifts between sites can seriously degrade model performance since models are prone to exploiting unstable correlations. Thus, many methods try to find features that a…
Knockout: A simple way to handle missing inputs
Minh Nguyen, Batuhan K. Karaman, Heejong Kim +3
Deep learning models benefit from rich (e.g., multi-modal) input features. However, multimodal models might be challenging to deploy, because some inputs may be missing at inferenc…
BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
Alan Q. Wang, Rachit Saluja, Heejong Kim +3
We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as…
Robust Learning via Conditional Prevalence Adjustment
Minh Nguyen, Alan Q. Wang, Heejong Kim +1
Healthcare data often come from multiple sites in which the correlations between confounding variables can vary widely. If deep learning models exploit these unstable correlations,…
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim +4
Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretabilit…
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Heejong Kim, Victor Ion Butoi, Adrian V. Dalca +2
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fas…
Learning to Compare Longitudinal Images
Heejong Kim, Mert R. Sabuncu
Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing…
Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI
Mengwei Ren, Heejong Kim, Neel Dey +1
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices…