Evidence map›Paper›PMID 41981002›Full record

ArticleScientific reports2026

Mixed attention mechanism multi-task learning for fetal abdominal standard plane recognition and key anatomical structure detection.

Yapeng Li, Yubing Huang, Zhonghua Liu, Tianen Fu, Shaozheng He, Yaocheng Wan, Yuling Fan, Peizhong Liu, Guorong Lyu, Shunlan Liu

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yapeng Li *College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
Yubing Huang *Department of Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
Zhonghua Liu *Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, 362000, China.
Tianen Fu *Department of Ultrasonics, Quanzhou Women's and Children's Hospital, Quanzhou, 362000, China.
Shaozheng HeDepartment of Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
Yaocheng WanDepartment of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130013, China.
Yuling FanCollege of Engineering, Huaqiao University, Quanzhou, 362021, China.
Peizhong LiuSchool of Medicine, Huaqiao University, Quanzhou, 362021, China. pzliu@hqu.edu.cn.
Guorong LyuDepartment of Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China. lgr_feus@sina.com.
Shunlan LiuDepartment of Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China. 85443785@qq.com.

Funding

Natural Science Foundation of Fujian Province 2021J011394, 2021J011404Quanzhou City Science and Technology Program 2022NS057, 2022C006RScientific Research Funds of Huaqiao University No. 60550Y23038
6 · The paper itself

Abstract

In prenatal ultrasound, accurately identifying fetal abdominal ultrasound standard planes (FAUSP) is challenging due to the complexity of anatomical structures. To address this, we developed FAUSP-NET, a multi-task network that integrates a mixed attention mechanism for real-time FAUSP recognition and anatomical structure detection. The network uses a residual backbone for feature extraction, enhanced by an attention mechanism and Large Selective Kernel Block (LSKblock) for better focus on key regions. A Focal_EIoU loss function addresses class imbalance and improves bounding box regression. Trained on 6767 FAUSP images, FAUSP-NET outperforms 24 popular models, achieving mAP@0.5 of 0.961 and mAP@0.5:0.95 of 0.653 in detection, with plane recognition accuracy of 0.972. Its average detection time is 24.1 ms. Doctor evaluations show that FAUSP-NET’s accuracy is comparable to senior physicians, offering significant support for clinical ultrasound diagnostics.

Indexed as

AbdomenFetusUltrasonography, PrenatalAlgorithmsFemaleHumansPregnancyDetection of key anatomical structuresFetal abdominal ultrasoundMixed attention mechanismMulti-task networkRecognition of standard planes

Identifiers

PMID41981002
PMCPMC13079838

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.