Chest pathology detection using deep learning with non-medical training

Proceedings - International Symposium on Biomedical Imaging

Volumn 2015-July, Issue , 2015, Pages 294-297

  Bar, Yaniv ^a   Diamant, Idit ^a   Wolf, Lior ^a   Lieberman, Sivan ^b   Konen, Eli ^b   Greenspan, Hayit ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b SHEBA MEDICAL CENTER   (Israel)

Author keywords

Chest Radiography; CNN; Computer Aided Diagnosis Disease Categorization; Deep Learning; Deep Networks

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; CONVOLUTIONAL NEURAL NETWORKS; IMAGE RECOGNITION; MEDICAL IMAGING; PATHOLOGY; X RAYS;

CHEST RADIOGRAPHS; CHEST RADIOGRAPHY; CLASSIFICATION APPROACH; DEEP ARCHITECTURES; DETECTING PATHOLOGIES; IMAGE REPRESENTATIONS; LEARNING APPROACH; MEDICAL IMAGE DATABASE;

DEEP LEARNING;

EID: 84943786510     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2015.7163871     Document Type: Conference Paper

References (15)

1. B. van Ginneken, L. Hogeweg, and M. Prokop, "Computer-aided diagnosis in chest radiography: Beyond nodules," European Journal of Radiology, vol. 72, no. 2, pp. 226-230, 2009.
2. J. M. Carrillo-de-Gea, G. García-Mateos, "Detection of Normality / Pathology on Chest Radiographs using LBP," In Bioinformatics, pp. 167-172, 2010
3. U. Avni, H. Greenspan, E. Konen, M. Sharon, and J. Goldberger, "X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words," IEEE Transactions on Medical Imaging, vol. 30, no. 3, pp. 733-746, 2011.
4. U. Avni, H. Greenspan, and J. Goldberger, "X-ray categorization and spatial localization of chest pathologies," Medical Image Computing and Computer-Assisted Intervention-MICCAI, Springer Berlin Heidelberg, pp. 199-206, 2011.
5. T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, no, 1, pp. 51-59, 1996.
6. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual categorization with bags of keypoints," Workshop on statistical learning in computer vision, ECCV, vol. 1, no. 1-22, pp. 1-2, 2004.
7. Y. LeCun, K. Kavukcuoglu, and C. Farabet, "Convolutional networks and applications in vision," Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253-256, 2010.
8. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN Features off-theshelf: An Astounding Baseline for Recognition," IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 512-519, 2014.
9. M. Oquab, L. Bottou, I. Laptev, and J. Sivic, "Learning and transferring mid-level image representations using convolutional neural networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1717-1724, 2014.
10. 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," Medical Image Computing and Computer-Assisted Intervention-MICCAI, Springer Berlin Heidelberg, pp. 246-253, 2013.
11. D. C. Cirean, A. Giusti, L. M. Gambardella, and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks," Medical Image Computing and Computer-Assisted Intervention-MICCAI, Springer Berlin Heidelberg, pp. 411-418, 2013.
12. 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.
13. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, pp. 1097-1105, 2012.
14. J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, "Imagenet: A largescale hierarchical image database," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248-255, 2009.
15. A. Oliva, and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," International journal of computer vision, vol. 42, no. 3, pp. 145-175, 2001.