79 citations · 187 across the 15 of their papers we have counts for
17 papers
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…
Subject-Specific Low-Field MRI Synthesis via a Neural Operator
Ziqi Gao, Nicha Dvornek, Xiaoran Zhang +3
Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field…
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…
Adaptive Correspondence Scoring for Unsupervised Medical Image Registration
Xiaoran Zhang, John C. Stendahl, Lawrence Staib +3
We propose an adaptive training scheme for unsupervised medical image registration. Existing methods rely on image reconstruction as the primary supervision signal. However, nuisan…
Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration
Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn +6
Deep learning methods for unsupervised registration often rely on objectives that assume a uniform noise level across the spatial domain (e.g. mean-squared error loss), but noise d…
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…
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Xiaoran Zhang, Chenyu You, Shawn Ahn +3
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explic…
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…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…
Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning
Bijie Bai, Hongda Wang, Yuzhu Li +14
The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagn…
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…
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences
Xiaoran Zhang, Michelle Noga, Kumaradevan Punithakumar
Myocardial characterization is essential for patients with myocardial infarction and other myocardial diseases, and the assessment is often performed using cardiac magnetic resonan…
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…