Distributed computing methodology for training neural networks in an image-guided diagnostic application

Computer Methods and Programs in Biomedicine

Volumn 81, Issue 3, 2006, Pages 228-235

  Plagianakos, V P ^a   Magoulas, G D ^b   Vrahatis, M N ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Backpropagation training; Distributed computing; Image guided diagnosis and surgery; Parallel implementations; Parallel virtual machine PVM

Indexed keywords

BACKPROPAGATION; ERROR ANALYSIS; IMAGE ANALYSIS; NEURAL NETWORKS; PROBLEM SOLVING; PROGRAM PROCESSORS;

BACKPROPAGATION TRAINING; IMAGE-GUIDED DIAGNOSIS AND SURGERY; PARALLEL IMPLEMENTATIONS; PARALLEL VIRTUAL MACHINE-PVM;

DISTRIBUTED COMPUTER SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COLONOSCOPY; COMPUTER; COMPUTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COLONOSCOPY; COMPUTER SIMULATION; COMPUTING METHODOLOGIES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PROGRAMMING LANGUAGES; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; SOFTWARE DESIGN; USER-COMPUTER INTERFACE;

EID: 33646065379     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2005.11.005     Document Type: Article

References (42)

1. Giess C., Mayer A., Evers H., and Meinzer H.P. Medical image processing and visualization on heterogenous clusters of symmetric multiprocessors using MPI and POSIX threads. Proceedings of IPPS/SPDP 98. Orlando (1998) 233-237
2. Breton V., Medina R., and Montagnat J. DataGrid, prototype of a biomedical grid. Methods Inf. Med. 42 (2003) 1-5
3. Cleary K., Clifford M., Freedman M., Zeng J., Mun S.K., Watson V., and Henderson F. Technology improvements for image-guided and minimally invasive spine procedures. IEEE Trans. Inf. Technol. Biomed. 6 (2002) 249-261
4. Maroulis D.E., Iakovidis D.K., Karkanis S.A., and Karras D.A. CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed. 70 (2003) 99-186
5. Delp S., Loan P., Basdogan C., and Rosen J.M. Surgical simulation: an emerging technology for training in emergency medicine. Presence 6 (1997) 147-159
6. Eldeib A.M., Ahmed M.N., Farag A.A., and Sites C.B. A web based system for surgical planning and simulation. Proc. SPIE 3517 (1998) 273-283
7. Farag A.A., and Sites C.B. Virtual endoscopy: modeling the navigation in 3D Brain volumes. Proceedings of the International Conference on Biomedical Engineering. Egypt (2002)
8. Kuhnapfel U., Cakmak H.K., and Maab H. Endoscopic surgery training using virtual reality and deformable tissue simulation. Comput. Graphics 24 (2000) 671-682
9. Geist A., Beguelin A., Dongarra J., Jiang W., Manchek R., and Sunderam V. PVM: Parallel Virtual Machine. A User's Guide and Tutorial for Networked Parallel Computing (1994), MIT Press, Cambridge
10. Plagianakos V.P., and Vrahatis M.N. Parallel evolutionary training algorithms for hardware-friendly neural networks. Nat. Comput. 1 (2002) 307-322
11. Plagianakos V.P., Magoulas G.D., and Vrahatis M.N. Evolutionary training of hardware realizable multilayer perceptrons. Neural Comput. Appl. 15 (2006) 33-40
12. Delaney P.M., Papworth G.D., and King R.G. Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon. In: Preedy V.R., and Watson R.R. (Eds). Methods in Disease: Investigating the Gastrointestinal Tract (1998), Greenwich Medical Media, London, UK
13. American Cancer Society, Cancer Facts and Figures, American Cancer Society, Atlanta, Georgia, publication no. 5008.00, 2000.
14. Parker S., Tong T., Bolden S., and Wingo P. Cancer statistics. CA Cancer J. Clin. 47 (1997) 5-27
15. Rex D., Weddle R., Pound D., O'Connor K., Hawes R., Dittus R., Lappas J., and Lumeng L. Flexible sigmoidoscopy plus air contrast barium enema versus colonoscopy for suspected lower gastrointestinal bleeding. Gastroenterology 98 (1990) 855-861
16. Karkanis S.A., Iakovidis D.K., Karras D.A., and Maroulis D.E. Computer aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Technol. Biomed. 7 (2003) 141-152
17. Karkanis S.A., Magoulas G.D., and Theofanous N. Image recognition and neuronal networks: intelligent systems for the improvement of imaging information. Minimally Invasive Ther. Allied Technol. 9 (2000) 225-230
18. Magoulas G.D., Plagianakos V.P., and Vrahatis M.N. Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Appl. Soft Comput. 4 (2004) 369-379
19. C.K. Kwoh, Probabilistic reasoning from correlated objective data, Ph.D. Thesis, Imperial College, London, UK, 1995.
20. Looney C.G. Pattern Recognition Using Neural Networks (1997), Oxford University Press, Oxford, UK
21. Esgiar A.N., Naguib R.N.G., Sharif B.S., Bennett M.K., and Murray A. Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans. Inf. Technol. Biomed. 2 (1998) 197-203
22. Karkanis S.A., Magoulas G.D., Iakovidis D.K., Karras D.A., and Maroulis D.E. Evaluation of textural feature extraction schemes for neural network-based interpretation of regions in medical images. Proceedings of the IEEE International Conference on Image Processing (ICIP). Thessaloniki, Greece (2001) 281-284
23. Karkanis S.A., Magoulas G.D., Iakovidis D.K., Maroulis D.E., and Theofanous N. Tumor recognition in endoscopic video images. Proceedings of the 26th EUROMICRO Conference. Maastricht, Netherlands (2000) 423-429
24. Daugman J.G. Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoustic, Speech Signal Process. 36 (1988) 1169-1179
25. Chen C.H. A study of texture classification using spectral features. Proceedings of the International Conference on Pattern Recognition. Munich, Germany (1982) 1074-1077
26. Gotlieb C.C., and Kreyszig K. Texture descriptors based on cooccurrence matrices, Computer Vision. Graphics Image Process. 51 (1990) 70-86
27. Haralick R.M. Statistical and structural approaches to texture. IEEE Proc. 67 (1979) 786-804
28. Cross G.R., and Jain A.K. Markov random field texture models. IEEE Trans. Pattern Anal. Machine Intell. 5 (1983) 25-39
29. Jacobs R.A. Increased rates of convergence through learning rate adaptation. Neural Networks 1 (1988) 295-307
30. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986), MIT Press, Cambridge, Massachusetts 318-362
31. Vogl T.P., Mangis J.K., Rigler J.K., Zink W.T., and Alkon D.L. Accelerating the convergence of the back-propagation method. Biol. Cybern. 59 (1988) 257-263
32. Plagianakos V.P., Magoulas G.D., and Vrahatis M.N. Deterministic nonmonotone strategies for effective training of multi-layer perceptrons. IEEE Trans. Neural Networks 13 (2002) 1268-1284
33. Lee Y., Oh S.-H., and Kim M.W. An analysis of premature saturation in backpropagation learning. Neural Networks 6 (1993) 719-728
34. Barzilai J., and Borwein J.M. Two point step size gradient methods. IMA J. Num. Anal. 8 (1998) 141-148
35. Plagianakos V.P., Sotiropoulos D.G., and Vrahatis M.N. Automatic adaptation of learning rate for backpropagation neural networks. In: Mastorakis N.E. (Ed). Recent Advances in Circuits and Systems (1998), World Scientific, Singapore 337-341
36. Leopold C. Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches (2000), John Wiley & Sons
37. The Beowulf Project, http://www.beowulf.org, last accessed 15/06/2004.
38. Sterling T.L., Salmon J., Becker D.J., and Savarese D.F. How to Build a Beowulf: A Guide to Implementation and Application of PC Clusters (1999), MIT Press, Cambridge
39. Coetzee L., and Botha E.C. An analysis of coarse-grain parallel training of a neural net. Network: Comput. Neural Syst. 6 (1995) 73-91
40. Anguita D., Boni A., and Parodi G. A case study of distributed high-performance computing system for neurocomputing. J. Syst. Architecture 46 (2000) 429-438
41. Plagianakos V.P., Vrahatis M.N., and Magoulas G.D. Nonmonotone methods for backpropagation training with adaptive learning rate. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN'99). Washington DC (1999) 2219-2223
42. Nguyen D., and Widrow B. Improving the learning speed of 2-layer neural network by choosing initial values of the adaptive weights. Proceedings of the IEEE First International Joint Conference on Neural Networks (1990) 21-26