Bodypart recognition using multi-stage deep learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9123, Issue , 2015, Pages 449-461

  Yan, Zhennan ^a,b   Zhan, Yiqiang ^a   Peng, Zhigang ^a   Liao, Shu ^a   Shinagawa, Yoshihisa ^a   Metaxas, Dimitris N ^b   Zhou, Xiang Sean ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MEDICAL IMAGING; NEURAL NETWORKS;

BODY PART RECOGNITION; CONTEXT-BASED; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; LEARNING SCHEMES; MANUAL ANNOTATION; MULTI-INSTANCE LEARNING; STATE-OF-THE-ART APPROACH;

COMPUTERIZED TOMOGRAPHY;

ADOLESCENT; ADULT; AGED; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHILD; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; FEMALE; HUMAN; IMAGE ENHANCEMENT; INFANT; MALE; MIDDLE AGED; PRESCHOOL CHILD; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; VERY ELDERLY; WHOLE BODY IMAGING; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHILD; CHILD, PRESCHOOL; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFANT; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED; WHOLE BODY IMAGING; YOUNG ADULT;

EID: 84983568260     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-19992-4_35     Document Type: Conference Paper

References (16)

1. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: CVPR, pp. 3642–3649. IEEE (2012)
2. Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
3. Donner, R., Menze, B. H., Bischof, H., Langs, G.: Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization. Med. Image Anal. 17 (8), 1304–1314 (2013)
4. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32 (9), 1627–1645 (2010)
5. Hong, L., Hong, S.: Methods and apparatus for automatic body part identification and localization. US Patent App. 11/933, 518, (15 May 2008)
6. Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
7. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, vol. 2, pp. 2169-2178. IEEE (2006)
8. Lowe, D. G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. (IJCV) 60 (2), 91–110 (2004)
9. Parikh, D.: Recognizing jumbled images: the role of local and global information in image classification. In: ICCV, pp. 519–526. IEEE (2011)
10. Park, J., Kang, G., Pan, S. B., Kim, P.: A novel algorithm for identification of body parts in medical images. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS (LNAI), vol. 4223, pp. 1148–1158. Springer, Heidelberg (2006)
11. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: NIPS, pp. 2553–2561 (2013)
12. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: Closing the gap to humanlevel performance in face verification. In: CVPR, pp. 1701–1708. IEEE (2014)
13. Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., Yan, S.: CNN: Single-label to multi-label (2014). arXiv preprint arXiv: 1406. 5726
14. Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801. IEEE (2009)
15. Zhan, Y., Zhou, X. S., Peng, Z., Krishnan, A.: Active scheduling of organ detection and segmentation in whole-body medical images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 313–321. Springer, Heidelberg (2008)
16. Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D. N., Zhou, X. S.: Towards robust and effective shape modeling: Sparse shape composition. Med. Image Anal. 16 (1), 265–277 (2012)