2 citations · 2 across the 4 of their papers we have counts for
6 papers
Beyond Site Agreement: Re-estimation for Brain Network Generalization
Yingxu Wang, Kunyu Zhang, Yanwu Yang3 +4
Cross-site out-of-distribution (OOD) generalization in resting-state functional magnetic resonance imaging (rs-fMRI) often relies on learning task-discriminative representations fr…
Learning beyond Site Bias for OOD Generalization in Brain Networks
Yingxu Wang, Kunyu Zhang, Yanwu Yang +5
Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-si…
Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis
Alexander Frotscher, Christian F. Baumgartner, Thomas Wolfers
Deep unsupervised anomaly detection in brain magnetic resonance imaging offers a promising route to identify pathological deviations without requiring lesion-specific annotations.…
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
Yanwu Yang, Guinan Su, Jiesi Hu +3
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range…
Hierarchical Characterization of Brain Dynamics via State Space-based Vector Quantization
Yanwu Yang, Thomas Wolfers
Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in capturing how the brain transitio…
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers +1
Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited…