Anatomy-specific classification of medical images using deep convolutional nets

Proceedings - International Symposium on Biomedical Imaging

Volumn 2015-July, Issue , 2015, Pages 101-104

  Roth, Holger R ^a   Lee, Christopher T ^a   Shin, Hoo Chang ^a   Seff, Ari ^a   Kim, Lauren ^a   Yao, Jianhua ^a   Lu, Le ^a   Summers, Ronald M ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Computed tomography (CT); Convolutional Networks; Deep Learning; Image Classification

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; COMPUTERIZED TOMOGRAPHY; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; IMAGE CLASSIFICATION; MEDICAL APPLICATIONS; MEDICAL IMAGING;

ANATOMICAL CLASSIFICATION; AREA UNDER THE CURVES; AUTOMATED CLASSIFICATION; CLASSIFICATION ERRORS; CLASSIFICATION PERFORMANCE; COMPUTER AIDED DIAGNOSIS SYSTEMS; CONVOLUTIONAL NETWORKS; STATE-OF-THE-ART METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 84944317431     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2015.7163826     Document Type: Conference Paper

References (16)

1. J. N., "Computer science: The learning machines," Nature, vol. 505(7482), pp. 146-8, 2014.
2. A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks," NIPS, pp. 1097-1105, 2012.
3. D. Ciresan, A. Giusti, L. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks," MICCAI, pp. 411-418, 2013.
4. A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, and M. Nielsen, "Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network," MICCAI, pp. 246-253, 2013.
5. H. Roth, L. Lu, A. Seff, K. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, and R. Summers, "A new 2. 5 d representation for lymph node detection using random sets of deep convolutional neural network observations," in MICCAI, pp. 520-527, Springer, 2014.
6. H. Roth, J. Yao, L. Lu, J. Stieger, J. Burns, and R. Summers, "Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications," MICCAI Spine Imaging Workshop, arXiv preprint arXiv:1407. 5976, 2014.
7. Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng, and M. Chen, "Medical image classification with convolutional neural network," ICARCV, 2014.
8. J. Feulner, S. K. Zhou, S. Seifert, A. Cavallaro, J. Hornegger, and D. Comaniciu, "Estimating the body portion of ct volumes by matching histograms of visual words," in SPIE Med. Imag., pp. 72591V-72591V, 2009.
9. V. Dicken, B. Lindow, L. Bornemann, J. Drexl, A. Nikoubashman, and H. Peitgen, "Rapid image recognition of body parts scanned in computed tomography datasets," IJCARS, vol. 5, no. 5, pp. 527-535, 2010.
10. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
11. A. Krizhevsky, "One weird trick for parallelizing convolutional neural networks," arXiv preprint arXiv:1404. 5997, 2014.
12. L. Van der Maaten and G. Hinton, "Visualizing data using t-sne," JMLR, vol. 9, no. 2579-2605, p. 85, 2008.
13. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, "Decaf: A deep convolutional activation feature for generic visual recognition," arXiv preprint arXiv:1310. 1531, 2013.
14. C. B. Akgül, D. Rubin, S. Napel, C. Beaulieu, H. Greenspan, and B. Acar, "Content-based image retrieval in radiology: current status and future directions," Digital Img., pp. 208-222, 2011.
15. H. Müller, T. Deselaers, T. Deserno, P. Clough, E. Kim, and W. Hersh, "Overview of the ImageCLEFmed 2006 medical retrieval and medical annotation tasks," LNCS, vol. 4730, pp. 595-608, 2007.
16. T. Deselaers and H. Ney, "Deformations, patches, and discriminative models for automatic annotation of medical radiographs," Pattern Recogn. Lett., vol. 29, no. 15, pp. 2003-2010, 2008.