Lawrence H. Staib — accepted AI-conference papers

2025

Progressive Test Time Energy Adaptation for Medical Image Segmentation

Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park, Daniel H. Pak, Anne-Marie Rickmann, Lawrence H. Staib +2

ICCV 2025poster

We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is challenging, as distribution shifts arise from inconsistent imaging protocols and patient variations. Unlike domain adaptatio…

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2025

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation

Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey

CVPR 2025poster

Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forward passes with dropout during inference. However, u…

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2024

Adaptive Correspondence Scoring for Unsupervised Medical Image Registration

Xiaoran Zhang*, John C. Stendahl, Lawrence H. Staib, Albert J. Sinusas, Alex Wong, James S. Duncan

ECCV 2024oral

"We propose an adaptive training scheme for unsupervised medical image registration. Existing methods rely on image reconstruction as the primary supervision signal. However, nuisance variables (e.g. noise and covisibility), violation of the Lambertian assumption in physical waves (e.g. ultrasound),…

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