most citedArtificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026★ 1 cited

Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

Yuanji Zhang, Yuhao Huang, Haoran Dou +28

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists…

cs.CV2024

Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification

Zhe Liu, Xiliang Zhu, Tong Han +7

Mitral regurgitation (MR) is a serious heart valve disease. Early and accurate diagnosis of MR via ultrasound video is critical for timely clinical decision-making and surgical int…