[1] 曹期龄,姜楞.经食道超声心动图的标准切面及其临床应用[J].中国医学影像技术,1989,5(2):8-11.(CAO Q L, JIANG L. Standard cross-section of transesophageal echocardiography and its clinical application[J]. Chinese Medical Imaging Technology, 1989,5(2):8-11.) [2] EBADOLLAHI S, CHANG S F, WU H. Automatic view recognition in echocardiogram videos using parts-based representation[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2004:2-9. [3] ZHOU K S, PARK J H, GEORGESCU B, et al. Image-based multiclass boosting and echocardiographic view classification[C]//Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2006:1559-1565. [4] OTEY M, BI J, KRISHNA S, et al. Automatic view recognition for cardiac ultrasound images[C]//Proceedings of the 2006 International Workshop on Computer Vision for Intravascular and Intracardiac Imaging. Copenhagen:[s.n.], 2006:187-194. [5] ROY A, SURAL S, MUKHERJEE J, et al. Modeling of echocardiogram video based on views and states[C]//Proceedings of the 5th Indian Conference on Computer Vision, Graphics and Image Processing. Berlin:Springer, 2006:397-408. [6] PARK J H, ZHOU S K, SIMOPOULOS C, et al. Automatic cardiac view classification of echocardiogram[C]//Proceedings of the 2007 IEEE 11th International Conference on Computer Vision. Piscataway, NJ:IEEE, 2007:1-8. [7] BEYMER D, SYEDA-MAHMOOD T, WANG F. Exploiting spatio-temporal information for view recognition in cardiac eCho videos[C]//Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Washington, DC:IEEE Computer Society, 2008:1-8. [8] WU H, BOWERS D, HUYNH T, et al. Echocardiogram view classification using low-level features[C]//Proceedings of the 2013 IEEE 10th International Symposium on Biomedical Imaging. Piscataway, NJ:IEEE, 2013:445-448. [9] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252. [10] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Redhool, NY:Curran Associates Inc., 2012:1097-1105. [11] RAZAVIAN A S, AZIZPOUR H, SULLIVAN J, et al. CNN features off-the-shelf:an astounding baseline for recognition[C]//Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ:IEEE, 2014:512-519. [12] OLIVA A, TORRALBA A. Modeling the shape of the scene:a holistic representation of the spatial envelope[J]. International Journal of Computer Vision, 2001, 42(3):145-175. [13] BAR Y, DIAMANT I, WOLF L, et al. Chest pathology detection using deep learning with non-medical training[C]//Proceedings of the 2015 IEEE International Symposium on Biomedical Imaging. Piscataway, NJ:IEEE, 2015:294-297. [14] MARGETA J, CRIMINISI A, LOZOYA R C, et al. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition[EB/OL].[2016-10-20].https://www.mendeley.com/research-papers/finetuned-convolutional-neural-nets-cardiac-mri-acquisition-plane-recognition/. [15] MAHENDRAN A, VEDALDI A. Understanding deep image representations by inverting them[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:5188-5196. [16] SIMONYAN K, VEDALDI A, ZISSERMAN A. Deep inside convolutional networks visualising image classification models and saliency maps[EB/OL].[2016-06-20]. http://www.robots.ox.ac.uk:5000/~vgg/publications/2014/Simonyan14a/simonyan14a.pdf. [17] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//Proceedings of the 13th European Conference on Computer Vision. Berlin:Springer, 2014:818-833. [18] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:2921-2929. [19] JARRETT K, KAVUKCUOGLU K, RANZATO M, et al. What is the best multi-stage architecture for object recognition?[C]//Proceedings of the 2009 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2009:2146-2153. [20] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:1-9. [21] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2016:171-180. [22] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916. [23] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details:delving deep into convolutional nets[EB/OL].[2016-06-20]. http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w04-DelvingDeepNets.pdf. [24] JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe:convolutional architecture for fast feature embedding[C]//Proceedings of the 2014 ACM International Conference on Multimedia. New York:ACM, 2014:675-678. This work is supported by the the Project of West Light Foundation of the Chinese Academy of Sciences (R&D and Application of Cardiac Function Evaluation System based on Medical Image Modeling).TAO Pan, born in 1988, Ph. D. candidate. His research interests include machine learning, medical image processing.FU Zhongliang, born in 1967, M.S., professor. His research interests include machine learning, data mining.ZHU Kai, born in 1991, Ph. D. candidate. His research interests include machine learning.WANG Lili, born in 1987, Ph. D. candidate. Her research interests include machine learning. |