activity
20242026
most citedDe-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation

3 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

eess.IV2025★ 1 cited

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…

eess.IV2024★ 3 cited

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…