中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer.
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Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer.
Chen, Hao; Ni, Dong; Yang, Xin; Li, Shengli; Heng, Pheng Ann
2014
Conference NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
AbstractAcquisition of the fetal abdominal standard plane (FASP) is crucial for prenatal ultrasound diagnosis. However, it requires a thorough knowledge of human anatomy and substantial experience. In this paper, we propose an automatic method to localize the FASP from US images. Unlike the previous methods that consider simple low-level features such as Haar features, we exploited the deep convolutional neural network to automatically learn the latent representation. In addition, we adopted the novel knowledge transfer method to enhance the learning performance by making use of the knowledge obtained in other domain. Experimental results on 219 fetal abdomen videos showed that the classification accuracy of our method was above 90%, outperforming other methods by a significant margin.
Department人机交互研究中心
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/5588
Collection集成所
Affiliation2014
Recommended Citation
GB/T 7714
Chen, Hao,Ni, Dong,Yang, Xin,et al. Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer.[C],2014.
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