9 citations · 22 across the 13 of their papers we have counts for
14 papers
WaveRNet: Wavelet-Guided Frequency Learning for Multi-Source Domain-Generalized Retinal Vessel Segmentation
Chanchan Wang, Yuanfang Wang, Qing Xu +1
Domain-generalized retinal vessel segmentation is critical for automated ophthalmic diagnosis, yet faces significant challenges from domain shift induced by non-uniform illuminatio…
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…
LapFM: A Laparoscopic Segmentation Foundation Model via Hierarchical Concept Evolving Pre-training
Qing Xu, Kun Yuan, Yuxiang Luo +4
Surgical segmentation is pivotal for scene understanding yet remains hindered by annotation scarcity and semantic inconsistency across diverse procedures. Existing approaches typic…
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…
How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment
Zhen Chen, Qing Xu, Jinlin Wu +7
Foundation models in video generation are demonstrating remarkable capabilities as potential world models for simulating the physical world. However, their application in high-stak…
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…
Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
Qing Xu, Wenting Duan, Zhen Chen
Histopathology image analysis is critical yet challenged by the demand of segmenting tissue regions and nuclei instances for tumor microenvironment and cellular morphology analysis…
Co-Seg++: Mutual Prompt-Guided Collaborative Learning for Versatile Medical Segmentation
Qing Xu, Yuxiang Luo, Wenting Duan +1
Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment a…
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…
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
Zhen Chen, Qing Xu, Xinyu Liu +1
In digital pathology, precise nuclei segmentation is pivotal yet challenged by the diversity of tissue types, staining protocols, and imaging conditions. Recently, the segment anyt…
DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rays
Qing Xu, Wenting Duan
Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thorac…