FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

• URL: http://arxiv.org/abs/2608.04766v1
• Date: Wed, 05 Aug 2026 12:33:07 GMT
• Title: FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
• Abstract summary: The lack of an ultrasound dataset on early fetal gestation has slowed down the development of automated assisted diagnosis.<n>We present a benchmark dataset for Fetal Ultrasound Screening in Early Pregnancy called FUSEP.<n>Our dataset consists of two ultrasound views recommended by the international guideline, totaling 4,017 ultrasound images, with 45,820 box-level expert-level annotations.
• Score: 50.7313801030297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently, fetal ultrasound screening is the most common modality for early pregnancy anatomy detection. This modality can detect anomalies earlier and provide opportune treatment advice. However, the lack of an ultrasound dataset on early fetal gestation has slowed down the development of automated assisted diagnosis. In this work, we present a benchmark dataset for Fetal Ultrasound Screening in Early Pregnancy to facilitate intelligent ultrasound examination and assisted diagnosis called FUSEP. Our dataset consists of two ultrasound views recommended by the international guideline, i.e., Crown-rump Length (CRL) and Nuchal Translucency (NT) views in three hospitals, totaling 4,017 ultrasound images, with 45,820 box-level expert-level annotations. Our dataset and baseline present the following three contributions: 1) Our medical experts annotated a total of 14 key anatomical structures in two views using a box-level format; 2) Our data is collected extensively from different sonographers, devices, scanning angles, hospitals, etc; 3) We report the performance of the semi-supervised learning, fully supervised learning, unsupervised domain adaptation (UDA), and source-free UDA in ultrasound images multi-object detection. To the best of our knowledge, this is the first publicly available dataset and benchmark for fetal early pregnancy ultrasound screening. We believe that FUSEP and benchmark can contribute to the medical community in the development of multiple tasks such as standard plane recognition, quality control on ultrasound images, automated assisted diagnostics in early fetal pregnancy, medical multi-object detection, domain adaptation for object detection, etc.

Related papers

• Echo-α: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation [76.6507710204181]
  We present Echo-, an agentic multimodal reasoning model for ultrasound interpretation.<n> Echo- is trained to coordinate organ-specific detector outputs, integrate them with global visual context, and convert the resulting evidence into grounded diagnostic decisions.<n>Results suggest that agentic multimodal reasoning can turn specialized detectors into verifiable clinical evidence.
  arXiv  Detail & Related papers  (2026-04-30T15:31:00Z)
• Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos [58.71502465551297]
  Intrapartum Ultrasound Grand Challenge (IUGC) co-hosted with MICCAI 2024 was launched.<n>IUGC introduces a clinically oriented multi-task automatic measurement framework that integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry.<n>The challenge releases the largest multi-center intrapartum ultrasound video dataset to date, comprising 774 videos (68,106 frames) collected from three hospitals.
  arXiv  Detail & Related papers  (2026-02-13T13:28:22Z)
• Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation [83.02147613524032]
  We introduce FetalMind, a medical AI system tailored to fetal ultrasound for both report generation and diagnosis.<n>We propose Salient Epistemic Disentanglement (SED), which injects an expert-curated bipartite graph into the model to decouple view-disease associations.<n>FetalMind outperforms open- and closed-source baselines across all gestational stages, achieving +14% average gains and +61.2% higher accuracy on critical conditions.
  arXiv  Detail & Related papers  (2025-10-14T19:57:03Z)
• A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications [77.3888788549565]
  We present EchoCare, a novel ultrasound foundation model for generalist clinical use.<n>We developed EchoCare via self-supervised learning on our curated, publicly available, large-scale dataset EchoCareData.<n>With minimal training, EchoCare outperforms state-of-the-art comparison models across 10 representative ultrasound benchmarks.
  arXiv  Detail & Related papers  (2025-09-15T10:05:31Z)
• Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound [28.27148137256845]
  We develop a case-level multiple instance learning (MIL)-based method for classifying fetal abdominal anomalies in prenatal ultrasound.<n>Our contribution is three-fold. First, we adopt a mixture-of-attention-experts module (MoAE) to weight different attention heads for various planes.<n> Secondly, we propose a medical-knowledge-driven feature selection module (MFS) to align image features with medical knowledge.
  arXiv  Detail & Related papers  (2025-07-02T06:31:26Z)
• Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging [11.261565838608488]
  Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy.<n>We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies.
  arXiv  Detail & Related papers  (2025-01-01T07:56:26Z)
• FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning [1.0819408603463427]
  More than 140M fetuses are born yearly, resulting in numerous scans. The availability of a large volume of fetal ultrasound scans presents the opportunity to train robust machine learning models. This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views.
  arXiv  Detail & Related papers  (2023-10-19T09:11:23Z)
• FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features [10.404128105946583]
  We present a novel fetus phantom ultrasound dataset, FPUS23, which can be used to identify the correct diagnostic planes for estimating fetal biometric values. The entire dataset is composed of 15,728 images, which are used to train four different Deep Neural Network models. We have also evaluated the models trained using our FPUS23 dataset, to show that the information learned by these models can be used to substantially increase the accuracy on real-world ultrasound fetus datasets.
  arXiv  Detail & Related papers  (2023-03-14T12:46:48Z)
• FetalNet: Multi-task deep learning framework for fetal ultrasound biometric measurements [11.364211664829567]
  We propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for fetal ultrasound scan video analysis. The main goal in fetal ultrasound video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Our method called FetalNet outperforms existing state-of-the-art methods in both classification and segmentation in fetal ultrasound video recordings.
  arXiv  Detail & Related papers  (2021-07-14T19:13:33Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.