Location of Repository

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

By V.P. Plagianakos, George D. Magoulas and M.N. Vrahatis


Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Topics: csis
Publisher: Elsevier
Year: 2006
OAI identifier: oai:eprints.bbk.ac.uk.oai2:503

Suggested articles



  1. (2003). DataGrid, Prototype of a Biomedical Grid,
  2. (2002). Technology improvements for image–guided and minimally invasive spine procedures, doi
  3. (2003). CoLD: A versatile detection system for colorectal lesions in endoscopy video-frames, doi
  4. (1997). Surgical simulation: An emerging technology for training in emergency medicine, doi
  5. (1998). A Web based System for Surgical Planning and Simulation, doi
  6. (2002). Virtual Endoscopy: Modeling the Navigation
  7. (2000). Endoscopic surgery training using virtual reality and deformable tissue simulation, doi
  8. (1994). PVM: Parallel Virtual Machine. A User’s Guide and Tutorial for Networked Parallel Computing, doi
  9. (2002). Parallel Evolutionary Training Algorithms for doi
  10. (2006). Evolutionary training of hardware realizable multilayer perceptrons, Neural Computing and Applications, doi
  11. (1998). Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon, doi
  12. Society, Cancer facts and figures, doi
  13. (1990). Flexible sigmoidoscopy plus air contrast barium enema versus colonoscopy for suspected lower gastrointestinal bleeding, doi
  14. (2003). Computer Aided Tumor Detection in Endoscopic Video using Color Wavelet Features, doi
  15. (2000). Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information, doi
  16. (2004). Neural Network-based Colonoscopic Diagnosis Using On-line Learning and Differential Evolution, doi
  17. (1995). Probabilistic reasoning from correlated objective data, Ph.D. Thesis, doi
  18. (1997). Pattern recognition using neural networks, doi
  19. (1998). Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa, doi
  20. (2001). Evaluation of textural feature extraction schemes for neural network–based interpretation of regions in medical images, doi
  21. (2000). Tumor recognition in endoscopic video images, doi
  22. (1988). Complete discrete 2D Gabor transforms by neural networks for image analysis and compression, doi
  23. (1982). A study of texture classification using spectral features,
  24. (1990). Texture descriptors based on cooccurrence matrices, doi
  25. (1979). Statistical and structural approaches to texture, doi
  26. (1983). Markov random field texture models, doi
  27. (1988). Increased rates of convergence through learning rate adaptation, doi
  28. (1986). Learning internal representations by error propagation, doi
  29. (1988). Accelerating the convergence of the back–propagation method, doi
  30. (2002). Deterministic Nonmonotone Strategies for Effective Training of Multi–Layer Perceptrons, doi
  31. (1993). An analysis of premature saturation in backpropagation learning, doi
  32. (1998). Two point step size gradient methods, doi
  33. (1998). Automatic adaptation of learning rate for backpropagation neural networks,
  34. (2000). Parallel and Distributed Computing: A Survey of Models, Paradigms and Approaches,
  35. The Beowulf Project, http://www.beowulf.org, last accessed 15/06/2004.
  36. (1999). How to build a Beowulf: A Guide to Implementation and Application of PC Clusters,
  37. (1995). An analysis of coarse–grain parallel training of a neural net, doi
  38. (2000). A case study of distributed high– performance computing system for neurocomputing, doi
  39. (1999). Nonmonotone Methods for Backpropagation Training with Adaptive Learning Rate, doi
  40. (1990). Improving the learning speed of 2–layer neural network by choosing initial values of the adaptive weights, doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.