Veritas: Combining expert opinions without labeled data

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 1, Issue , 2008, Pages 45-52

  Cholleti, Sharath R ^a   Goldman, Sally A ^a   Blum, Avrim ^b   Politte, David G ^c   Don, Steven ^c  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^b Carnegie Mellon University   (United States)

^c WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC RADIOGRAPHY; ELECTRIC INSTRUMENT TRANSFORMERS; KNOWLEDGE BASED SYSTEMS; TOMOGRAPHY;

ARTIFICIAL DATUMS; COMPUTED TOMOGRAPHIES; COMPUTER ALGORITHMS; CRITICAL PROBLEMS; CT IMAGES; EXPERT OPINIONS; GOLD STANDARDS; GROUND TRUTHS; HUMAN CHESTS; LABELED DATUMS; LUNG NODULES; VERITAS;

MEDICAL IMAGING;

EID: 57649173427     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2008.141     Document Type: Conference Paper

References (16)

1. D. Angluin and P. Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.
2. S. G. Armato, M. F. McNitt-Gray, A. P. Reeves, C. R. Meyer, G. McLennan, D. R. Aberle, E. A. Kazerooni, H. MacMa-hon, E. J. van Beek, D. Yankelevitz, E. A. Hoffman, C. I. Henschke, R. Y. Roberts, M. S. Brown, R. M. Engelmann, R. C. Pais, C. W. Piker, D. Qing, M. Kocherginsky, B. Y. Croft, and L. P. Clarke. The lung image database consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology, 14(11):1409-1421, Nov. 2007.
3. S. G. Armato, R. Y. Roberts, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, G. McLennan, R. M. Engelmann, P. H. Bland, D. R. Aberle, E. A. Kazerooni, H. MacMahon, E. J. van Beek, D. Yankelevitz, B. Y. Croft, and L. P. Clarke. The lung image database consortium (LIDC): Ensuring the integrity of expert-defined "truth". Academic Radiology, 14(12):1455-1463, Dec. 2007.
4. A. Blum. Empirical support for Winnow and weighted-majority algorithms: Results on a calendar scheduling domain. Machine Learning, 26(1):5-23, 1997.
5. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 92-100, New York, NY, 1998. ACM Press.
6. N. Cesa-Bianchi and G. Lugosi. Prediction, Learning, and Games. Cambridge University Press, New York, NY, 2006.
7. M. Collins and Y. Singer. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pages 100-111, 1999.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum-likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39:1-38, 1977.
9. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System Sciences, 55(1): 119-139, 1997.
10. D. Greig, B. Porteous, and H. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistics Society, B, 51:271-279, 1989.
11. C. C. Jaffe. Lecture to American college of radiology. IEEE Transactions on Medical Imaging, l(4):226-229, 1982.
12. M. J. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. The MIT Press, Cambridge, Massachusetts, 1994.
13. F. W. Prior, B. J. Erickson, and L. Tarbox. Open source software projects of the caBIG in vivo imaging workspace software special interest group. Journal of Digital Imaging, 20(1):94-100, Nov. 2007.
14. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999.
15. S. K. Warfield, K. H. Zhou, and W. M. Wells. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging, 23(7):903-921, 2004.
16. T. Yoo, M. Ackerman, W. Lorensen, W. Schroeder, V. Cha-lana, S. Aylward, D. Metaxes, and R. Whitaker. Engineering and algorithm design for an image processing API: A technical report on ITK - the Insight Toolkit, 2002.