12 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…
A physics-informed foundation model for quantitative diffusion MRI
Zihan Li, Jialan Zheng, Ziyu Li +18
Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-bra…
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…
GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT
Shuo Jiang, Yuhao Hong, Chunbo Jiang +9
Grounding radiology report descriptions to 3D CT volumes is essential for verifiable clinical interpretation, yet remains challenging due to the semantic-spatial gap between free-t…
TabFORGE: A Tabular Foundation Model for Generative Modelling
Xiangjian Jiang, Mingxuan Liu, Nikola Simidjievski +2
Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, rather than optimisation for…
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
Yuanhan Wang, Yifei Chen, Beining Wu +7
Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensit…
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…
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
Xuguang Bai, Mingxuan Liu, Tongxi Song +6
Chest computed tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imaging increasingly exceed what c…
FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis
Xiaotian Hu, Junwei Huang, Mingxuan Liu +9
Fetal ultrasound (US) is the primary imaging modality for prenatal screening, yet its interpretation relies heavily on the expertise of the clinician. Despite advances in deep lear…
Beyond hospital reach: Autonomous lightweight ultrasound robot for liver sonography
Zihan Li, Yixiao Xu, Lei Zhang +8
Liver disease is a major global health burden. While ultrasound is the first-line diagnostic tool, liver sonography requires locating multiple non-continuous planes from positions…