9 citations · 13 across the 11 of their papers we have counts for
14 papers
FreqDINO++: A Frequency-Guided Multi-Task Routing Vision Foundation Model for Universal Ultrasound Analysis
Qing Xu, Yixuan Zhang, Yue Li +7
Ultrasound image analysis plays a crucial role in cancer screening and prenatal diagnosis, yet comprehensive assessment requires jointly addressing tasks such as lesion segmentatio…
DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation
Jiaxuan Li, Qing Xu, Xiangjian He +4
Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under r…
Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation
Qing Xu, Xiangjian He, Wenting Duan +2
Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remarkable universal feature repr…
Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark
Yuan Tian, Yue Li, Wei Xia +10
Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate d…
RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection
Tao Wu, Qing Xu, Xiangjian He +3
Roadside litter poses environmental, safety and economic challenges, yet current monitoring relies on labour-intensive surveys and public reporting, providing limited spatial cover…
FreqDINO: Frequency-Guided Adaptation for Generalized Boundary-Aware Ultrasound Image Segmentation
Yixuan Zhang, Qing Xu, Yue Li +6
Ultrasound image segmentation is pivotal for clinical diagnosis, yet challenged by speckle noise and imaging artifacts. Recently, DINOv3 has shown remarkable promise in medical ima…
UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
Yue Li, Qing Xu, Yixuan Zhang +9
The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images. However, its performance on ultrasound images is significantly de…
TM-UNet: Token-Memory Enhanced Sequential Modeling for Efficient Medical Image Segmentation
Yaxuan Jiao, Qing Xu, Yuxiang Luo +3
Medical image segmentation is essential for clinical diagnosis and treatment planning. Although transformer-based methods have achieved remarkable results, their high computational…
CoMA: Complementary Masking and Hierarchical Dynamic Multi-Window Self-Attention in a Unified Pre-training Framework
Jiaxuan Li, Qing Xu, Xiangjian He +6
Masked Autoencoders (MAE) achieve self-supervised learning of image representations by randomly removing a portion of visual tokens and reconstructing the original image as a prete…
Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation
Yuxiang Luo, Qing Xu, Hai Huang +5
The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anatomical subregions. While the rec…
MambaVesselNet++: A Hybrid CNN-Mamba Architecture for Medical Image Segmentation
Qing Xu, Yanming Chen, Yue Li +4
Medical image segmentation plays an important role in computer-aided diagnosis. Traditional convolution-based U-shape segmentation architectures are usually limited by the local re…
HER-Seg: Holistically Efficient Segmentation for High-Resolution Medical Images
Qing Xu, Zhenye Lou, Chenxin Li +6
High-resolution segmentation is critical for precise disease diagnosis by extracting fine-grained morphological details. Existing hierarchical encoder-decoder frameworks have demon…
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
Zhenye Lou, Qing Xu, Zekun Jiang +6
Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image…
De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
Qing Xu, Jiaxuan Li, Xiangjian He +8
The universality of deep neural networks across different modalities and their generalization capabilities to unseen domains play an essential role in medical image segmentation. T…