Probabilistic lung nodule classification with belief decision trees

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 4493-4498

  Zinovev, Dmitriy ^a   Feigenbaum, Jonathan ^a   Furst, Jacob ^a   Raicu, Daniela ^a  

^a DEPAUL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; COMPUTED TOMOGRAPHY SCAN; COMPUTER AIDED DIAGNOSTICS; DATA SETS; GROUND TRUTH; HIGH-LEVEL INFORMATION; IMAGE DATABASE; LUNG CT; LUNG NODULE; NATIONAL CANCER INSTITUTE; SEMANTIC ANNOTATIONS;

ALGORITHMS; BIOLOGICAL ORGANS; CLASSIFICATION (OF INFORMATION); DECISION TREES; FORESTRY; SEMANTICS; TOMOGRAPHY; TREES (MATHEMATICS);

COMPUTERIZED TOMOGRAPHY;

ALGORITHMS; CLASSIFICATION; DATA BASES; DECISION THEORY; FORESTRY; PERFORMANCE;

ARTICLE; CLASSIFICATION; COMPUTER ASSISTED TOMOGRAPHY; DECISION TREE; HUMAN; LUNG TUMOR; PROBABILITY; RADIOGRAPHY;

DECISION TREES; HUMANS; LUNG NEOPLASMS; PROBABILITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84863596692     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6091114     Document Type: Conference Paper

References (21)

1. S. G. Armato III, et al., "Lung Image Database Consortium: developing a resource for the medical imaging research community," Radiology, vol. 232, 2004, pp. 739-748.
2. C. Muramatsu, Q. Li, K. Suzuki, et al., "Investigation of psychophysical measure for evaluation of similar images for mammographic masses: Preliminary results," Med Phys, vol. 32, 2005, pp. 2295-2304.
3. T. Li, C. Zhang, S. Zhu, "Empirical studies on multi-label classification," Proc. ICTAI, 2006, pp. 86-92.
4. N. Ghamrawi, A. McCallum, "Collective multi-label classification," Proc ACM CIKM, 2005, pp. 195-200.
5. C. Ramachandran, R. Malik, X. Jin, J. Gao, K. Nahrstedt, J. Han, "VideoMule: a consensus learning approach to multi-label classification from noisy user-generated videos," Proc. 17th ACM Int. Conf. on multimedia, 2009, pp. 721-724.
6. R. Jin, Z. Ghahramani, "Learning with multiple labels," Proc NIPS, 2002, pp. 897-904.
7. R. Snow, B. O'Connor, D. Jurafsky, A. Y Ng, "Cheap and fast - but is it good?: evaluating non-expert annotations for natural language tasks," Proc. Conf. on Empirical Methods in Natural Language Processing, 2008 Jan, pp. 254-263.
8. V. Sheng, F. Provost, P. Ipeirotis, "Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers," Proc. 14th ACM SIGKDD Int. Conf. on Knowledge discovery and Data Mining, 2008, pp. 614-622.
9. B. Kanefsky, NG. Barlow, VC. Gulick, "Can distributed volunteers accomplish massive data analysis task?," Lunar Planet. Sci. Conf., 2001, [CD-ROM] 31:1272.
10. V. C. Raykar, S. Yu, L. H. Zhao, A. Jerebko, C. Florin, G. H. Valadez, L. Bogoni, L. Moy, "Supervised learning from multiple experts: whom to trust when everyone lies a bit," Proc. 26th Annual Int. Conf. on Machine Learning, 2009, pp. 889-896.
11. M. S. Bjanger, T. Denoeux, "Induction of decision trees from partially classified data using belief functions," Master thesis at University of Compiegne. 2000
12. P. Vannoorenberghe, T. Denoeux, "Handling Uncertain Labels in Multiclass Problems using Belief Decision Trees," Proc. Int. Conf. on Processing and Management of Uncertainty, vol. 3, 2002 July, pp. 1916-1926.
13. B. Quost, T. Denoeux, "Learning from data with uncertain labels by boosting credal classifiers," In Proc. 1st ACM SIGKDD workshop on knowledge discovery from uncertain data, 2009 June, pp. 38-47.
14. T. Denoeux, "A Neural Network Classifier Based on Dempster-Shafer Theory," IEEE Trans SMCA, part A, 2000, pp. 131-150.
15. G. Shafer, A mathematical theory of evidence, 1976; Princeton University Press, Princeton, NJ.
16. Z. Elouedi, K. Mellouli, P. Smets, "Belief decision trees: Theoretical foundations," Int J Approx Reason, vol. 28, 2001, pp. 91-124.
17. D. Zinovev, D. Raicu, J. Furst, S. G. Armato III, "Predicting radiological panel opinions using a panel of machine learning classifiers," Algorithms Journal, vol. 2, 2009, pp. 1473-1502.
18. J. R. Quinlan, "Improved Use of Continuous Attributes in C4.5," J Artif Intell Res, vol. 4, 1996, pp 77-90.
19. K. A. Spackman, "Signal detection theory: Valuable tools for evaluating inductive learning," Proc. 6th Int. Workshop on Machine Learning, 1989, pp. 160-163.
20. H. Liu, D. Song, S. Rüger, R. Hu, V. Uren, "Comparing dissimilarity measures for content-based image retrieval," Proc. 4th Asia Inf. Ret. Conf. on Information Retrieval Technology, 2008 Jan, pp. 44-50.
21. J. Puzicha, T. Hofmann, J. M. Buhmann, "Non-parametric similarity measures for unsupervised texture segmentation and image retrieval," Proc. IEEE CVPR, 1997 Jun, pp. 267-272.