1 citations · 1 across the 7 of their papers we have counts for
7 papers
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
Jingxian Xu, Yuhao Huang, Rusi Chen +2
Accurate landmark localization in medical images is a fundamental step for quantitative clinical measurement and downstream analysis. Existing localization methods have advanced, a…
AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis
Jian Wang, Yang Yang, Ziheng Pan +4
Life-limiting congenital anomalies require accurate prenatal diagnosis for appropriate clinical decision-making. Prenatal ultrasound (US) examinations involve multiple anatomical p…
Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation
Lingrui Li, Yanfeng Zhou, Nan Pu +2
Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation mod…
Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos
Jiaheng Zhou, Yanfeng Zhou, Wei Fang +3
Ultrasound videos are an important form of clinical imaging data, and deep learning-based automated analysis can improve diagnostic accuracy and clinical efficiency. However, the s…
XNet v2: Fewer Limitations, Better Results and Greater Universality
Yanfeng Zhou, Lingrui Li, Zichen Wang +3
XNet introduces a wavelet-based X-shaped unified architecture for fully- and semi-supervised biomedical segmentation. So far, however, XNet still faces the limitations, including p…
Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures
Jiaxing Huang, Yanfeng Zhou, Yaoru Luo +3
Accurate segmentation of long and thin tubular structures is required in a wide variety of areas such as biology, medicine, and remote sensing. The complex topology and geometry of…
Robust Source-Free Domain Adaptation for Fundus Image Segmentation
Lingrui Li, Yanfeng Zhou, Ge Yang
Unsupervised Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled training data to the target domain with only unlabe…