5 papers
Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration
Che Liu, Yinda Chen, Haoyuan Shi +21
The advent of large-scale vision foundation models, pre-trained on diverse natural images, has marked a paradigm shift in computer vision. However, how the frontier vision foundati…
ETA: Energy-based Test-time Adaptation for Depth Completion
Younjoon Chung, Hyoungseob Park, Patrick Rim +7
We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when t…
Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
Daniel H. Pak, Shubh Thaker, Kyle Baylous +3
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures…
Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation
Xiaoran Zhang, Eric Z. Chen, Lin Zhao +6
We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation. Existing ultrasound segmentation methods often struggle w…
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…