79 citations · 187 across the 15 of their papers we have counts for
9 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…
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…
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…
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min +5
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pa…
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Chenyu You, Jinlin Xiang, Kun Su +5
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model th…
Fully Automated Left Atrium Segmentation from Anatomical Cine Long-axis MRI Sequences using Deep Convolutional Neural Network with Unscented Kalman Filter
Xiaoran Zhang, Michelle Noga, David Glynn Martin +1
This study proposes a fully automated approach for the left atrial segmentation from routine cine long-axis cardiac magnetic resonance image sequences using deep convolutional neur…
A Comparative Study for Non-rigid Image Registration and Rigid Image Registration
Xiaoran Zhang, Hexiang Dong, Di Gao +1
Image registration algorithms can be generally categorized into two groups: non-rigid and rigid. Recently, many deep learning-based algorithms employ a neural net to characterize n…