99 citations · 107 across the 10 of their papers we have counts for
10 papers
CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations
Zixian Su, Hongkai Zhang, Fan Gao +12
Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on…
BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging
Taiping Qu, Hongkai Zhang, Lantian Zhang +18
Cardiac magnetic resonance (CMR) is a cornerstone for diagnosing cardiovascular disease. However, it remains underutilized due to complex, time-consuming interpretation across mult…
Un-mixing Test-time Adaptation under Heterogeneous Data Streams
Zixian Su, Jingwei Guo, Xi Yang +2
Deploying deep models in real-world scenarios remains challenging due to significant performance drops under distribution shifts between training and deployment environments. Test-…
Navigating Distribution Shifts in Medical Image Analysis: A Survey
Zixian Su, Jingwei Guo, Xi Yang +4
Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements support…
SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Kai Yao, Zhaorui Tan, Zixian Su +3
Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open…
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering
Jingwei Guo, Kaizhu Huang, Xinping Yi +2
Whilst spectral Graph Neural Networks (GNNs) are theoretically well-founded in the spectral domain, their practical reliance on polynomial approximation implies a profound linkage…
Unraveling Batch Normalization for Realistic Test-Time Adaptation
Zixian Su, Jingwei Guo, Kai Yao +3
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to…
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation
Zixian Su, Kai Yao, Xi Yang +3
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previou…
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
Zixian Su, Kai Yao, Xi Yang +4
Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in th…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…