From the 1 of 17 linked papers with an AI index.
17 papers
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Yuhao Huang, Yuanji Zhang, Yuhuan Lu +3
Assessment of ventriculomegaly (VM) on fetal brain ultrasound relies primarily on measuring lateral ventricular atrial width on standard planes, which is operator-dependent and may…
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
The paper introduces AnomExpert, a framework that learns to identify anatomical planes and select disease-relevant planes in prenatal ultrasound images using only case-level labels…
FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection
Rusi Chen, Yuhao Huang, Hongyuan Zhang +4
Accurate detection of end-systole (ES) and end-diastole (ED) frames is fundamental to echocardiographic assessment. Existing methods are typically developed in a view-specific mann…
Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation
Le Ou, Xiliang Zhu, Huanwen Liang +8
Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, particularly in low-resource…
Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
Huanwen Liang, Yuhao Huang, Xiliang Zhu +6
Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal…