14 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…
TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration
Yang Zhou, Wenxue Li, Peng Zhang +3
Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods ofte…
MorVess: Morphology-Aware Pulmonary Vessel Segmentation Network
Fuyou Mao, Yifei Chen, Beining Wu +9
Accurate pulmonary vessel segmentation remains challenging due to the sparse, tortuous, and multi-scale nature of vascular structures, where small branches are easily lost and topo…
HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT
Numan Saeed, Salma Hassan, Shahad Hardan +27
Head and neck cancers (HNC) represent a significant global health burden, with accurate tumor delineation being essential for effective radiotherapy planning. The complexity of the…
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…
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…
AnyAD: Unified Any-Modality Anomaly Detection in Incomplete Multi-Sequence MRI
Changwei Wu, Yifei Chen, Yuxin Du +7
Reliable anomaly detection in brain MRI remains challenging due to the scarcity of annotated abnormal cases and the frequent absence of key imaging modalities in real clinical work…
autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
Junwei Huang, Yingqi Hao, Yitong Luo +6
Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer vari…