5 papers · 1 filter
Topology-Informed Prompt-Conditioned Universal Segmentation of Uterine Structures from Ultrasound and MRI
Yongheng Sun, Yuexi Gu, Jingwen Sun +2
Multi-structure segmentation of the uterus is important for computer-assisted screening, diagnosis, and treatment planning of uterine diseases, where ultrasound and MRI provide com…
Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
Yuexi Gu, Mengqi Wu, Yongheng Sun +3
Ultrasound is a non-invasive, real-time, and cost-effective imaging technique widely used in clinical diagnosis. However, its diagnostic efficacy is often compromised by inherent s…
Unified Multi-Site Multi-Sequence Brain MRI Harmonization Enriched by Biomedical Semantic Style
Mengqi Wu, Yongheng Sun, Qianqian Wang +2
Aggregating multi-site brain MRI data can enhance deep learning model training, but also introduces non-biological heterogeneity caused by site-specific variations (e.g., differenc…
Learning from Heterogeneous Structural MRI via Collaborative Domain Adaptation for Late-Life Depression Assessment
Yuzhen Gao, Qianqian Wang, Yongheng Sun +3
Accurate identification of late-life depression (LLD) using structural brain MRI is essential for monitoring disease progression and facilitating timely intervention. However, exis…
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation
Yongheng Sun, Mingxia Liu, Chunfeng Lian
Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-t…