Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

IEEE Journal of Biomedical and Health Informatics

Volumn 19, Issue 5, 2015, Pages 1627-1636

  Chen, Hao ^a   Ni, Dong ^b   Qin, Jing ^b   Li, Shengli ^c   Yang, Xin ^b   Wang, Tianfu ^b   Heng, Pheng Ann ^a,d  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b Shenzhen University   (China)

^c Affiliated Baoan Hospital of Nanfang Medical University   (China)

^d SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

convolutional neural network; deep learning; domain transfer; knowledge transfer; standard plane; Ultrasound

Indexed keywords

CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP NEURAL NETWORKS; KNOWLEDGE MANAGEMENT; MEDICAL IMAGING; MEDICAL PROBLEMS; SAMPLING; TRANSFER LEARNING; ULTRASONICS;

ANATOMICAL STRUCTURES; AUTOMATIC LOCALIZATION; CLASSIFICATION PERFORMANCE; DOMAIN TRANSFERS; KNOWLEDGE TRANSFER; LEARNING-BASED APPROACH; MEDICAL IMAGE COMPUTING; STATE-OF-THE-ART METHODS;

DEEP LEARNING;

ABDOMEN; ARTIFICIAL NEURAL NETWORK; ECHOGRAPHY; FEMALE; FETUS; FETUS ECHOGRAPHY; HUMAN; IMAGE PROCESSING; PHYSIOLOGY; PREGNANCY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC;

ABDOMEN; FEMALE; FETUS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PREGNANCY; ROC CURVE; ULTRASONOGRAPHY, PRENATAL;

EID: 84940970126     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2015.2425041     Document Type: Article

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