activity
20172025
most citedPatch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

316 citations · 510 across the 31 of their papers we have counts for

collaborators
Showing cs.CVShow all

22 papers · 1 filter

cs.CV2025

nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection

Alexandra Ertl, Stefan Denner, Robin Peretzke +8

Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…

cs.CV20231 cited

PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation

Ao Chang, Xing Tao, Xin Yang +5

Interactive medical image segmentation refers to the accurate segmentation of the target of interest through interaction (e.g., click) between the user and the image. It has been w…

cs.CV2023

Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction

Mingyuan Luo, Xin Yang, Zhongnuo Yan +7

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper un…

cs.CV2023

Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos

Xinrui Zhou, Yuhao Huang, Wufeng Xue +7

Localization of the narrowest position of the vessel and corresponding vessel and remnant vessel delineation in carotid ultrasound (US) are essential for carotid stenosis grading (…

cs.CV2023

Instructive Feature Enhancement for Dichotomous Medical Image Segmentation

Lian Liu, Han Zhou, Jiongquan Chen +5

Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance.…

cs.CV2023

Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification

Yuhao Huang, Xin Yang, Xiaoqiong Huang +7

Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep…