5 papers · 1 filter
Spatially-Aware Evaluation of Segmentation Uncertainty
Tal Zeevi, Eléonore V. Lieffrig, Lawrence H. Staib +1
Uncertainty maps highlight unreliable regions in segmentation predictions. However, most uncertainty evaluation metrics treat voxels independently, ignoring spatial context and ana…
Progressive Test Time Energy Adaptation for Medical Image Segmentation
Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park +5
We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is chall…
Causal Modeling of fMRI Time-series for Interpretable Autism Spectrum Disorder Classification
Peiyu Duan, Nicha C. Dvornek, Jiyao Wang +2
Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects social and communicative behaviors. It emerges in early life and is generally associated wi…
Towards Zero-Shot Task-Generalizable Learning on fMRI
Jiyao Wang, Nicha C. Dvornek, Peiyu Duan +2
Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-st…
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
Tal Zeevi, Lawrence H. Staib, John A. Onofrey
Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal spac…