Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection

Artificial Intelligence in Medicine

Volumn 29, Issue 1-2, 2003, Pages 39-60

  Antal, Peter ^a   Fannes, Geert ^a   Timmerman, Dirk ^a   Moreau, Yves ^a   De Moor, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bayesian multilayer perceptron; Belief network; Classification with rejection; Informative prior distribution; Knowledge acquisition

Indexed keywords

MEDICINE; MULTILAYER NEURAL NETWORKS; STATISTICAL METHODS; TUMORS;

BLACK-BOX CLASSIFIERS;

ARTIFICIAL INTELLIGENCE;

ADULT; ASSAY; BAYES THEOREM; CONFERENCE PAPER; CONTROLLED STUDY; FEMALE; HUMAN; INFORMATION; INTERMETHOD COMPARISON; LEARNING; MAJOR CLINICAL STUDY; MALIGNANT NEOPLASTIC DISEASE; METHODOLOGY; MODEL; OVARY CARCINOMA; OVARY TUMOR; PARAMETER; PREDICTION; PRIORITY JOURNAL; PROBABILITY; SAMPLE; SCIENCE; STATISTICAL ANALYSIS; TECHNIQUE; TUMOR CLASSIFICATION;

EID: 0041334207     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(03)00053-8     Document Type: Conference Paper

References (61)

1. Abu-Mostafa Y.S., Hints and the VC dimension. Neural Comput. 5(2):1993;278-288.
2. Van Allen T, Greiner R, Hooper P. Bayesian error-bars for belief net inference. In: Breese J, Koller D, editors. Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI-2001). Morgan Kaufmann; 2001. p. 522-9.
3. Altman R.B., Challenges for intelligent systems in biology. IEEE Intell. Syst. 16(6):2002;14-18.
4. Antal P. Applicability of prior domain knowledge formalised as Bayesian network in the process of construction of a classifier. In: Proceedings of the 24th Annual Conference of the IEEE Industrial Electronic Society (IECON '98); 1998. p. 2527-31.
5. Antal P, Fannes G, Moreau Y, Timmerman D, De Moor B. Using literature and data to learn Bayesian networks as clinical models of ovarian tumors. Artif Intell Med, 2003, in press. Special issue on Bayesian Models Med.
6. Antal P, Fannes G, Verrelst H, De Moor B, Vandewalle J. Incorporation of prior knowledge in black-box models: comparison of transformation methods from Bayesian network to multilayer perceptrons. In: Workshop on Fusion of Domain Knowledge with Data for Decision Support, 16th Uncertainty in Artificial Intelligence Conference; 2000. p. 42-8.
7. Antal P, Glenisson P, Fannes G, Mathijs J, Moreau Y, De Moor B. On the potential of domain literature for clustering and Bayesian network learning. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM-KDD-2002); 2002. p. 405-14.
8. Antal P, Meszaros T, De Moor B, Dobrowiecki T. Domain knowledge based information retrieval language: an application of annotated Bayesian networks in ovarian cancer domain. In: Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS-2002); 2002. p. 213-8.
9. Antal P, Verrelst H, Timmerman D, Moreau Y, Van Huffel S, De Moor B, Vergote I. Bayesian networks in ovarian cancer diagnosis: potential and limitations. In: Proceedings of the 13th IEEE Symposium on Computer-Based Medical System (CBMS-2000); 2000. p. 103-9.
10. Bakerman R, Gottman JM. Observing interaction: an introduction to sequential analysis. Cambridge: Cambridge University Press; 1986.
11. Berk, JS, Hacker NF (Eds.). Practical gynecologic oncology. 2nd ed. Baltimore, MD: Williams and Wilkins; 1995.
12. Bernardo JM. Bayesian theory. Wiley: Chichester; 1995.
13. Bishop CM. Neural networks for pattern recognition. Oxford: Clarendon Press; 1995.
14. Blumer A., Ehrenfeucht A., Haussler D., Warmuth M.K., Learnability and the Vapnik-Chervonenkis dimension. J. ACM. 36(4):1989;929-965.
15. Cheng J., Greiner R., Learning Bayesian belief network classifiers: algorithms and system. Lect. Notes Comput. Sci. 2056:2001;141-151.
16. Claus E.B., Risch N., Thompson W.D., Autosomal dominant inheritance of early-onset breast cancer. Cancer. 73(3):1994;643-650.
17. Cloete I, Zurada JM. Knowledge-based neurocomputing. Cambridge, MA: MIT Press; 2000.
18. Cooper G.F., Herskovits E., A Bayesian method for the induction of probabilistic networks from data. Machine Learn. 9:1992;309-347.
19. Dagum P., Luby M., Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artif. Intell. 60:1993;141-153.
20. Dasgupta S., The sample complexity of learning fixed-structure Bayesian networks. Machine Learn. 29:1997;165-180.
21. Devroye L, Györfi L, Lugosi G. A probabilistic theory of pattern recognition. Berlin: Springer; 1996.
22. Easton D.F., Ford D., Bishop D.T., Breast and ovarian cancer incidence in BRCA1-mutation. Am. J. Hum. Genet. 56:1995;265-271.
23. Finkler N.J., Benaceraf B., Lavin P.T., Comparison of serum CA 125, clinical impression, and ultrasound in the preoperative evaluation of ovarian masses. Obstetrics Gynecol. 72(4):1998;659-663.
24. Friedman N., Geiger D., Goldszmidt M., Bayesian networks classifiers. Machine Learn. 29:1997;131-163.
25. Friedman N, Koller D. Being Bayesian about network structure. In: Boutilier C, Goldszmidt M, editors. Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI-2000). Morgan Kaufmann; 2000. p. 201-11.
26. Friedman N, Yakhini Z. On the sample complexity of learning Bayesian networks. In: Horvitz E, Jensen Finn V, editors. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI-1996). Morgan Kaufmann; 2000. p. 274-82.
27. Geiger D, Heckerman D. A characterization of the Dirichlet distribution with application to learning Bayesian networks. In: Besnard P, Hanks S, editors. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI-1995). Morgan Kaufmann; 2000. p. 196-207.
28. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. London: Chapman & Hall; 1995.
29. Geman S., Bienenstock S., Doursat R., Neural networks and the bias/variance dilemma. Neural Comput. 4:1992;1-58.
30. Granberg S., Wikland M., Jansson I., Macroscopic characterization of ovarian tumors and the relation to the histological diagnosis. Gynecol. Oncol. 35:1989;139-144.
31. Hand DJ. Construction and assessment of classification rules. Chichester: Wiley; 1997.
32. Hanley J.A., McNeil B.J., The meaning and use of the area under receiver operating characteristic (ROC) curve. Radiology. 143:1982;29-36.
33. Harris R, Whittemore AS, Itnyre J, and the Collaborative Ovarian Cancer Group. Characteristics relating to ovarian cancer risk (i, ii, iii, iv). Am J Epidemiol 1992;136:1175-1220.
34. Haussler D., Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artif. Intell. 36:1988;177-221.
35. Haussler D., Bounds on the sample complexity of Bayesian learning using information theory and the Vapnik-Chervonenkis dimension. Machine Learn. 14:1994;83-113.
36. Heckerman D., Geiger D., Chickering D., Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learn. 20:1995;197-243.
37. Huang C, Darwiche A. Inference in belief networks: a procedural guide. Amsterdam: Elsevier. Int J Approx Reason 1996;5:225-63.
38. Langston A.A., Hereditary ovarian cancer. Gynecol. Oncol. Pathol. 9:1997;3-7.
39. Lucas P. Restricted Bayesian network structure learning. In: Blockeel H, Denecker M, editors. Proceedings of 14th Belgian-Dutch Conference on Artificial Intelligence (BNAIC '02); 2002. p. 211-8.
40. Mitchell T.M., Does machine learning really work? AI Mag. 18:1997;11-20.
41. Müller P., Insua R.D., Issues in Bayesian analysis of neural network models. Neural Comput. 10:1998;571-592.
42. Myllymaki P. Mapping Bayesian networks to stochastic neural networks: a foundation for hybrid Bayesian-neural systems. Ph.D. dissertation, University of Helsinki, No. A-1995-1; 1995.
43. Neal RM. Bayesian learning for neural networks. Berlin: Springer; 1996.
44. Neal RM. Transferring prior information between models using imaginary data. Technical Report No. 0108, Department of Statistics, University of Toronto, July 2001.
45. Niyogi P., Poggio T., Girosi F., Incorporating prior information in machine learning by creating virtual examples. Proc. IEEE. 86(11):1998; 2196-2209.
46. Pearl J. Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann; 1988.
47. Ripley B. Stochastic simulation. Chichester: Wiley; 1987.
48. Rüger SM, Ossen A. Clustering in weight space of feedforward nets. In: von der Malsburg C, editor. ICANN 96, Lecture Notes in Computer Science. Berlin: Springer; 1996. p. 83-8.
49. Shavlik JW. An overview of research at Wisconsin on knowledge-based neural networks. In: Proceedings of the International Conference on Neural Networks; 1996. p. 65-9.
50. Sowmya R. Theory refinement of Bayesian networks with hidden variables. In: Proceedings of 15th International Conference on Machine Learning; 1998.
51. Spiegelhalter D.J., Local computations with probabilities on graphical structures and their application to expert systems. J. R. Statist. Soc. B. 50(2):1988;157-224.
52. Spiegelhalter D.J., Dawid A., Lawritzen S., Cowell R., Bayesian analysis in expert systems. Statist. Sci. 8(3):1993;219-283.
53. Tekay A., Jouppila P., Validity of pulsatility and resistance indices in classification of adnexal tumors with transvaginal color Doppler ultrasound. Ultrasound Obstetrics Gynecol. 2:1992;338-344.
54. Timmerman D. Ultrasonography in the assessment of ovarian and tamoxifen-associated endometrial pathology. Ph.D. Dissertation, Leuven University Press, D/1997/1869/70; 1997.
55. Timmerman D., Artificial neural network models for the pre-operative discrimination between malignant and benign adnexal masses. Ultrasound Obstetrics Gynecol. 13:1999;17-25.
56. Timmerman D., Valentin L., Bourne T.H., Collins W.P., Verrelst H., Vergote I., Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the international ovarian tumor analysis (IOTA) group. Ultrasound Obstetrics Gynecol. 16(5):2000;500-505.
57. Towell G., Shavlik J., Knowledge-based artificial neural networks. Artif. Intell. 70:1994;119-165.
58. National Cancer Institute (US). SEER cancer data; 1998.
59. Valentin L., Gray scale sonography, subjective evaluation of the color Doppler image and measurement of blood flow velocity for distinguishing benign and malignant tumors of suspected adnexal origin. Eur. J. Obstetrics Gynecol. Reprod. Biol. 72:1997;63-72.
60. Vapnik VN. The nature of statistical learning theory. Berlin: Springer; 1995.
61. Whittemore A.S., Gong G., Itnyre J., Prevalence and contribution of BRCA1 mutations in breast cancer and ovarian cancer. Am. J. Hum. Genet. 60:1997;496-504.