Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 2098-2105

  Llorá, Xavier ^a   Reddy, Rohith ^a,b   Matesic, Brian ^b   Bhargava, Rohit ^a,b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Genetics based machine learning; Learning classifier Sys tems; Parallelization; Prostate cancer

Indexed keywords

DIAGNOSIS; INFRARED SPECTROSCOPY; MOLECULAR VIBRATIONS; PIXELS;

GENETICS-BASED MACHINE LEARNING; LEARNING CLASSIFIER SYSTEMS; PARALLELIZATION; PROSTATE CANCER;

BIOMEDICAL ENGINEERING;

EID: 34548100580     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277366     Document Type: Conference Paper

References (34)

1. G. Amdahl. Validity of the single processor approach to achieving large-scale computing capabilities. In Proceedings of the American Federation of Information Processing Societies Conference (AFIPS), volume 30, pages 483-485. AFIPS, 1967.
2. M. Amin, D. Grignon, P. Humphrey, and J. Srigley. Gleason Grading of Prostate Cancer: A Contemporary Approach. Lippincott Williams & Wilkins: Philadelphia, 2004.
3. J. Bacardit and M. Butz. Advances at the frontier of Learning Classifier Systems (Volume I), chapter Data Mining in Learning Classifier Systems: Comparing XCS with GAssist, page in press. Springer-Verlag, 2006.
4. J. Bacardit and N. Krasnogor. Biohel: Bioinformatics-oriented hierarchical evolutionary learning. Nottingham eprints, University of Nottingham, 2006.
5. A. Barry and J. Drugowitsch. LCSWeb: the LCS wiki, 1997. http://lcsweb.cs.bath.ac.uk/.
6. E. Bernadó, X. Llorà, and J. Garrell. Advances in Learning Classifier Systems: 4th International Workshop (IWLCS 2001), chapter XCS and GALE: a Comparative Study of Two Learning Classifier Systems with Six Other Learning Algorithms on Classification Tasks, pages 115-132. Springer Berlin / Heidelberg, July 2001.
7. R. Bhargava, D. Fernandez, S. Hewitt, and I. Levin. High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. Biochemica et Biophisica Acta, pages 830-845, 2006.
8. E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, 2000.
9. O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena. Genetic Fuzzy Systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, 2001.
10. J. Epstein, P. Walsh, and F. Sanfilippo. Clinical and Cost Impact of Second-opinion Pathology: Review of Prostate Biopsies Prior to Radical Prostatectomy. American Journal of Surgical Pathology, 20:851-857, 1996.
11. D. Fernandez, R. Bhargava, S. Hewitt, and I. Levin. Infrared spectroscopic imaging for histopathologic recognition. Nature Biotechnology, 23(4):469-474, 2005.
12. I. Flockhart. GA-MINER: parallel data mining with hierarchical genetic algorithms (final report). Technical Report Technical Report EPCCAIKMS-GA-MINER-REPORT 1.0, University of Edinburgh, 1995.
13. E. Gabriel, G. Fagg, G. Bosilca, T. Angskun, J. Dongarra, J. Squyres, V. Sahay, P. Kambadur, B. Barrett, A. Lumsdaine, R. Castain, D. Daniel, R. Graham, and T. Woodall. Open MPI: Goals, concept, and design of a next generation MPI implementation. In Proceedings of the 11th European PVM/MPI Users' Group Meeting. Springer, 2004.
14. D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional., 1989.
15. D. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Springer, 2002.
16. S. J. Jacobsen, S. K. Katusic, E. J. Bergstralh, J. E. Oesterling, O. Del, G. G. Klee, C. G. Chute, and M. M. Lieber. Incidence of prostate cancer diagnosis in the eras before and after serum prostate-specific antigen testing. JAMA, 274:1445-1449, 1995.
17. V. Kumar, A. Grama, A. Gupta, and G. Karpis. Introduction to Parallel Computing: Design and Analysis of Parallel Algorithms. Benjamin-Cummings Publishing Company, 1994.
18. J.-B. Lattouf and F. Saad. Gleason score on biopsy: is it reliable for predcting the final grade on pathology? BJU International, 90:694-699, 2002.
19. I. Levin and R. Bhargava. Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition. Annual Review of Physical Chemistry, 56:429-474, 2005.
20. X. Llorà. Learning Classifier Systems and other Genetics-Based Machine Learning Blog, 2006. http://www-illigal.ge.uiuc.edu/lcs-n-gbml/.
21. X. Llorà. Genetics-Based Machine Learning using Fine-grained Parallelism for Data Mining. PhD thesis, Enginyeria i Arquitectura La Salle. Ramon Llull University, Barcelona, Catalonia, European Union, February, 2002.
22. X. Llorà and J. Garrell. Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '2001), pages 461-468. Morgan Kaufmann Publishers, 2001.
23. X. Llorà and D. Goldberg. Bounding the effect of noise in multiobjective learning classifier systems. Evolutionary Computation Journal, 11(3):279-298, 2003.
24. X. Llorà, A. Priya, and R. Bhargava. Observer-invariant histopathology using genetics-based machine learning. Technical report, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (IliGAL TR No 200627), 2006.
25. X. Llorà and K. Sastry. Fast rule matching for learning classifier systems via vector instructions. In Proceedings of the 2006 Genetic and Evolutionary Computation Conference, pages 1513-1520. ACM Press, 2006.
26. X. Llorà, K. Sastry, and D. Goldberg. The compact classifier system: Motivation, analysis and first results. In Proceedings of the Congress on Evolutionary Computation, volume 1, pages 596-603. IEEE press, 2005. (Also as IlliGAL TR No. 2005019).
27. X. Llorà, K. Sastry, D. Goldberg, and L. de la Ossa. The X-ary extended compact classifier system: Linkage learning in Pittsburgh LCS. In Advances at the frontier of Learning Classifier Systems (Volume II), page in preparation. Springer, 2007. IlliGAL report no. 2006015.
28. C. J. Merz and P. M. Murphy. UCI repository for machine learning data-bases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
29. W. Murphy, I. Rivera-Ramirez, L. Luciani, and Z. Wajsman. Second opinion of anatomical pathology: A complex issue not easily reduced to matters of right and wrong. J. Urol, 165:1957-1959, 2001.
30. J. Nguyen, D. Schultz, A. Renshaw, R. Vollmer, W. Welch, K. Cote, and A. D'Amico. The impact of pathology review on treatment recommendations for patients with adenocarcinoma of the prostate. Urologic Oncology: Seminars and Original Investigations, 22:295-299, 2004.
31. A. C. Society. How Many Men Get Prostate Cancer?, 2006. http://www.cancer.org/docroot/CRI/content/ CRI_2_2_1X_How_many_men_get_prostate_cancer_36.asp?rnav=cri.
32. S. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
33. S. Wilson. Get reall XCS with continuous-valued inputs. Lecture Notes in Computer Science, 1813:209-219, 2000.
34. I. H. Witten and E. Frank. Data Mining. Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA., 2000.