Applying deep-layered clustering to mammography image analytics

Proceedings of the 2010 Biomedical Science and Engineering Conference, BSEC 2010: Biomedical Research and Analysis in Neuroscience, BRAiN

Volumn , Issue , 2010, Pages

  Rose, Derek C ^a   Arel, Itamar ^a   Karnowski, Thomas P ^a   Paquit, Vincent C ^b  

^a University of Tennessee   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BREAST CANCER; BUILDING BLOCKES; COMPUTER AIDED DETECTION; DEEP LEARNING; FALSE NEGATIVES; FALSE POSITIVE; HIERARCHY OF CLUSTERING; HIGH DIMENSIONAL DATA; IMAGE FEATURES; IMAGE PATCHES; LAYERED CLUSTERING; MAMMOGRAPHIC IMAGES; MAMMOGRAPHY IMAGES; MICROCALCIFICATIONS; REGIONS OF INTEREST; UNSUPERVISED CLUSTERING;

MAMMOGRAPHY; NEURAL NETWORKS; RESEARCH; X RAY SCREENS;

CLUSTER ANALYSIS;

EID: 77955648349     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BSEC.2010.5510799     Document Type: Conference Paper

References (10)

1. Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Machine Learning, vol. 2:1, pp. 1-127, 2009.
2. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, 1998, pp. 2278-2324.
3. G. E. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets." Neural Computation, vol. 18, pp. 1527-1554, 2006.
4. H. Lee, R. Grosse, R. Ranganath, and A. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in Proceedings of the 26th International Conference on Machine Learning, 2009.
5. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, "Extracting and composing robust features with denoising autoencoders," in Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML08), 2008, pp. 1096-1103.
6. J. Dengler, S. Behrens, and J. F. Desaga, "Segmentation of microcalcifications in mammograms," IEEE Transactions of Medical Imaging, vol. 12, no. 4, pp. 634-642, December 1993.
7. M. R. L. et. al, "A dual-energy subtraction technique for microcalcification imaging in digital mammography - a signal-to-noise analysis," Medical Physics, vol. 29, no. 8, pp. 1739-1751, Aug 2002.
8. C. L. Christiansen and F. W. et. al, "Predicting the cumulative risk of false-positive mammograms," Journal of the National Cancer Institute, vol. 92, no. 20, pp. 1657-1666, October 2000.
9. I. Arel, D. Rose, and R. Coop, "Destin: A scalable deep learning architecture with application to high-dimensional robust pattern recognition," in Proceedings of AAAI 2009 Fall Symposium on Biologically Inspired Cognitive Architectures (BICA), November 2009.
10. S. Young, I. Arel, T. Karnowski, and D. Rose, "A fast and stable incremental clustering algorithm," in 7th International Conference on Information Technology (ITNG), April 2010.