9 citations · 14 across the 12 of their papers we have counts for
15 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…
OFD-Net: Teacher-Free Reliable Semi-supervised Medical Image Segmentation with Orthogonal Feature Disentanglement Net of Foreground-Background
Shao-feng Jiang, Zhe-yang Jing, Qin Lu +4
Semi-supervised learning (SSL) is an effective solution for medical image segmentation with limited annotations. Existing SSL methods mainly rely on pseudo-labels generated by teac…
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…
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…
SP-Det: Self-Prompted Dual-Text Fusion for Generalized Multi-Label Lesion Detection
Qing Xu, Yanqian Wang, Xiangjian Hea +5
Automated lesion detection in chest X-rays has demonstrated significant potential for improving clinical diagnosis by precisely localizing pathological abnormalities. While recent…
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…
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…
Endora: Video Generation Models as Endoscopy Simulators
Chenxin Li, Hengyu Liu, Yifan Liu +6
Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for machine learning. Despite progress in generating 2D medical…