6 papers · 1 filter
Improving Medical Image Generative Models with Fréchet Distance Loss
Andrew Marshall, Xuanang Xu, Xiaoran Zhang +3
Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture complex morphological charac…
MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI
Xinran Li, Junlin Yang, Annabella Shewarega +4
Manual reporting of 3D MRI studies is time-consuming, yet end-to-end structured report generation for 3D liver MRI remains underexplored due to volumetric complexity and scarce pai…
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
Junlin Yang, Tian Yu, Nicha C. Dvornek +6
Hepatocellular carcinoma (HCC) is biologically heterogeneous, shaped by the interplay between hepatic functional reserve and tumor-related oncologic factors; thus, similar survival…
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…
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…