5 papers
Generalizable fetal brain biometry via adaptive artificial intelligence
Yijin Li, Mingxuan Liu, Yi Liao +24
Reliable fetal brain biometry on magnetic resonance imaging (MRI) is essential for diagnosing congenital brain anomalies, but artificial intelligence models often lose accuracy und…
ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora
Xinfeng Zhang, Mingxuan Liu, Yifei Chen +9
Structured reporting converts free-text radiology narratives into queryable data keys, facilitating cohort assembly, longitudinal tracking, and training label generation for medica…
Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration
Xiaotian Hu, Mingxuan Liu, Junwei Huang +13
Automated fetal ultrasound interpretation requires a workflow from visual perception, including plane recognition and anatomical segmentation, to clinical understanding, including…
INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
Xiaotian Hu, Mingxuan Liu, Hongjia Yang +10
Spatio-temporal fetal brain atlases are important for characterizing normative neurodevelopment and identifying congenital anomalies. However, existing atlas construction pipelines…
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Mingxuan Liu, Yingqi Hao, Yi Liao +17
Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment, yet its manual diagnosis and lesion segment…