6 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…
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…
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…
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…