most citedA Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation

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cs.CV2025

DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image

Ziwei Zhao, Zhixing Zhang, Yuhang Liu +4

In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous…

cs.CV2025

GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs

Zhao Zhang, Ziwei Zhao, Dong Wang +1

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from t…

cs.CV20222 cited

A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation

Shijia Huang, Feng Li, Hao Zhang +3

Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored…

cs.CV2022

Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection

Ziwei Zhao, Dong Wang, Yihong Chen +2

Detecting mass in mammogram is significant due to the high occurrence and mortality of breast cancer. In mammogram mass detection, modeling pairwise lesion correspondence explicitl…

cs.CV2022

PointScatter: Point Set Representation for Tubular Structure Extraction

Dong Wang, Zhao Zhang, Ziwei Zhao +3

This paper explores the point set representation for tubular structure extraction tasks. Compared with the traditional mask representation, the point set representation enjoys its…