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# Fetal Ultrasound Analysis

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medRxiv Sep 27, 2026
Fetal ultrasound screening learns from scarce labels: RadarMAE's radar-style masking holds cross-hospital metric drops to 0.04
First-trimester ultrasound images for Down syndrome screening are low-resolution and structurally variable, and confirmed rare cases are scarce, so standard self-supervised pretraining struggles. RadarMAE instead masks alternating concentric rings and angular sectors, a radar-like pattern that forces the network to learn the continuous curvature and elongated shapes of craniofacial structures, reaching AUC 0.99 and 97% accuracy on an internal cohort. With domain-adversarial adaptation, the model calibrates on unlabeled images from four external hospitals and limits the drop in key metrics to 0.04 or less.
First-trimester ultrasound images for Down syndrome screening are low-resolution and structurally variable, and confirmed rare cases are scarce, so standard self-supervised pretraining struggles. RadarMAE instead masks alternating concentric rings and angular sectors, a radar-like pattern that forces the network to learn the continuous curvature and elongated shapes of craniofacial structures, reaching AUC 0.99 and 97% accuracy on an internal cohort. With domain-adversarial adaptation, the model calibrates on unlabeled images from four external hospitals and limits the drop in key metrics to 0.04 or less.
West China Second University Hospital of Sichuan University Central South University Sichuan University Fudan University Capital Medical University Chinese Academy of Sciences Third Affiliated Hospital of Zhengzhou University Beijing Obstetrics and Gynecology Hospital
Yantong Zhu（First author）Hua Chen（Corresponding author）Qingqing Wu（Corresponding author）
RadarMAE Domain Adaptation Fetal Ultrasound Analysis Masked Autoencoder Grad-CAM
arXiv Sep 25, 2026
FetalAgents orchestrates specialized fetal-ultrasound models through multiple agents, achieving top performance across eight clinical tasks on external validation and auto-generating structured reports
The work proposes FetalAgents, a multi-agent system built on AutoGen in which a GPT-5-mini-driven Coordinator parses clinical intent and anatomical plane and dynamically dispatches expert agents that combine specialized vision models such as FetalCLIP, nnU-Net, USFM, SAMUS, and AoP-SAM via deterministic fusion rules, while a Summarizer consolidates outputs into structured reports; across eight clinical tasks (standard plane classification, brain plane classification, abdomen and stomach segmentation, AoP estimation, AC and HC measurement, GA prediction) on multi-center external datasets, FetalAgents achieves the best or most competitive results compared with specialized vision models, ultrasound foundation models, and general/medical MLLMs, and supports end-to-end video-stream keyframe ext
The work proposes FetalAgents, a multi-agent system built on AutoGen in which a GPT-5-mini-driven Coordinator parses clinical intent and anatomical plane and dynamically dispatches expert agents that combine specialized vision models such as FetalCLIP, nnU-Net, USFM, SAMUS, and AoP-SAM via deterministic fusion rules, while a Summarizer consolidates outputs into structured reports; across eight clinical tasks (standard plane classification, brain plane classification, abdomen and stomach segmentation, AoP estimation, AC and HC measurement, GA prediction) on multi-center external datasets, FetalAgents achieves the best or most competitive results compared with specialized vision models, ultrasound foundation models, and general/medical MLLMs, and supports end-to-end video-stream keyframe ext
West China Second University Hospital of Sichuan University Sichuan University University of California San Diego Tsinghua University
Mingxuan Liu（First author）Junwei Huang（First author）Xiaotian Hu（First author）Qiyuan Tian（Corresponding author）
FetalAgents Multi-agent system Fetal Ultrasound Analysis Video Summarization Fetal Biometry

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