Incremental Learning Versus Batch Learning for Classification of User’s Behaviour in Medical Imaging

Carlos Viana-Ferreira, Sérgio Matos, Carlos Costa

Abstract

Communication latency still hinders the adoption of Cloud computing paradigms in medical imaging environments where it could serve as a reliable technology to support repository outsourcing solutions or inter-institutional workflows, for instance. One way to overcome this is by implementing cache repositories and prefetching mechanisms. Nevertheless, such solutions are usually based on static rules that may inefficiently manage the cache storage capacity. For that reason, this paper compares a pattern recognition system using incremental learning versus batch learning, in order to assess which one could be more appropriately used in a medical imaging cache mechanism.

References

  1. ACR-NEMA 2011a. Digital Imaging and Communications in Medicine. Part 4: Service Class Specifications. Rosslyn, VA, USA: NEMA.
  2. ACR-NEMA 2011b. Digital Imaging and Communications in Medicine (DICOM). Rosslyn, VA: National Electrical Manufacturers Association.
  3. Ali, W., Shamsuddin, S. M. & Ismail, A. S. 2011. A survey of Web caching and prefetching. Int. J. Advance. Soft Comput. Appl, 3, 18-44.
  4. Bui, A. A., Mcnitt-Gray, M. F., Goldin, J. G., Cardenas, A. F. & Aberle, D. R. 2001. Problem-oriented prefetching for an integrated clinical imaging workstation. Journal of the American Medical Informatics Association, 8, 242-253.
  5. Cao, P. & Irani, S. Cost-aware WWW proxy caching algorithms. Proceedings of the 1997 USENIX Symposium on Internet Technology and Systems, 1997.
  6. Chen, C.-H., Pau, L.-F. & Wang, P. S.-P. 2010. Handbook of pattern recognition and computer vision, World Scientific.
  7. Chen, Y. & Sion, R. 2011. To cloud or not to cloud?: musings on costs and viability. Proceedings of the 2nd ACM Symposium on Cloud Computing. Cascais, Portugal: ACM.
  8. Costa, C., Freitas, F., Pereira, M., Silva, A. & Oliveira, J. L. 2009. Indexing and retrieving DICOM data in disperse and unstructured archives. International Journal of Computer Assisted Radiology and Surgery, 4, 71-77.
  9. Duda, R. O., Hart, P. E. & Stork, D. G. 2012. Pattern classification, John Wiley & Sons.
  10. Feio, M. J., Viana-Ferreira, C. & Costa, C. 2013. Combining Multiple MAChine Learning Algorithms to Predict Taxa Under Reference Conditions For Streams Bioassessment. River Research and Applications, n/a-n/a.
  11. Guresen, E., Kayakutlu, G. & Daim, T. U. 2011. Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38, 10389-10397.
  12. Huang, H. 2011. PACS and imaging informatics: basic principles and applications, Wiley-Blackwell.
  13. Jaleel, A., Theobald, K. B., Simon C. Steely, J. & Emer, J. 2010. High performance cache replacement using rereference interval prediction (RRIP). Proceedings of the 37th annual international symposium on Computer architecture. Saint-Malo, France: ACM.
  14. Liu Sheng, O. R., Wei, C.-P., Hu, P. J.-H. & Chang, N. 2000. Automated learning of patient image retrieval knowledge: neural networks versus inductive decision trees. Decision Support Systems, 30, 105-124.
  15. Maji, P. & Pal, S. K. 2011. Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging, John Wiley & Sons.
  16. Marques Godinho, T., Viana-Ferreira, C., Bastiao Silva, L. & Costa, C. 2014. A Routing Mechanism for Cloud Outsourcing of Medical Imaging Repositories. Biomedical and Health Informatics, IEEE Journal of, PP, 1-1.
  17. Pal, S. K. & Pal, A. 2001. Pattern recognition: from classical to modern approaches, World Scientific.
  18. Philbin, J., Prior, F. & Nagy, P. 2011. Will the Next Generation of PACS Be Sitting on a Cloud? Journal of Digital Imaging, 24, 179-183.
  19. Pianykh, O. S. 2011. Digital imaging and communications in medicine (DICOM), Springer.
  20. Podlipnig, S. & Boszormenyi, L. 2003. A survey of Web cache replacement strategies. ACM Comput. Surv., 35, 374-398.
  21. Psounis, K. & Prabhakar, B. A randomized Web-cache replacement scheme. INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001 2001. 1407-1415 vol.3.
  22. Ramírez, J., Górriz, J. M., Salas-Gonzalez, D., Romero, A., López, M., Álvarez, I. & Gómez-Río, M. 2013. Computer-aided diagnosis of Alzheimer's type dementia combining support vector machines and discriminant set of features. Information Sciences, 237, 59-72.
  23. Rengier, F., Mehndiratta, A., Tengg-Kobligk, H., Zechmann, C. M., Unterhinninghofen, R., Kauczor, H. U. & Giesel, F. L. 2010. 3D printing based on imaging data: review of medical applications. International Journal of Computer Assisted Radiology and Surgery, 5, 335-341.
  24. Silva, L. A. B., Costa, C. & Oliveira, J. L. 2013a. An agile framework to support distributed medical imaging scenarios. IEEE International Conference on Healthcare Informatics 2013 (ICHI 2013). Philadelphia, USA.
  25. Silva, L. B., Costa, C. & Oliveira, J. 2013b. DICOM relay over the cloud. International Journal of Computer Assisted Radiology and Surgery, 8, 323-333.
  26. Smith, A. J. 1982. Cache memories. ACM Computing Surveys (CSUR), 14, 473-530.
  27. Sutton, L. N. 2011. PACS and diagnostic imaging service delivery-A UK perspective. European Journal of Radiology, 78, 243-249.
  28. Sylva, P. 2010. A Situation Analysis on PACS prospects for a Developing Nation. Sri Lanka Journal of BioMedical Informatics, 1, 112-117.
  29. Valente, F., Costa, C. & Silva, A. 2013. Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval. PLoS ONE, 8, e61888.
  30. Valente, F., Viana-Ferreira, C., Costa, C. & Oliveira, J. L. 2012. A RESTful Image Gateway for Multiple Medical Image Repositories. Information Technology in Biomedicine, IEEE Transactions on, 16, 356-364.
  31. Viana-Ferreira, C. & Costa, C. 2014a. Challenges of Using Cloud Computing in Medical Imaging. In: Ramachandran, M. (ed.) Advances in cloud Computing Research.
  32. Viana-Ferreira, C. & Costa, C. 2014b. DICOM Traffic Generator based on behavior profiles. In: IEEE (ed.) IEEE-EMBS International Conferences on Biomedical and Health Informatics. Valencia, Spain.
  33. Yakami, M., Ishizu, K., Kubo, T., Okada, T. & Togashi, K. 2011. Development and Evaluation of a Low-Cost and High-Capacity DICOM Image Data Storage System for Research. Journal of Digital Imaging, 24, 190-195.
  34. Yegnanarayana, B. 2009. Artificial neural networks, PHI Learning Pvt. Ltd.
Download


Paper Citation


in Harvard Style

Viana-Ferreira C., Matos S. and Costa C. (2015). Incremental Learning Versus Batch Learning for Classification of User’s Behaviour in Medical Imaging . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 431-438. DOI: 10.5220/0005219704310438


in Bibtex Style

@conference{healthinf15,
author={Carlos Viana-Ferreira and Sérgio Matos and Carlos Costa},
title={Incremental Learning Versus Batch Learning for Classification of User’s Behaviour in Medical Imaging},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={431-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005219704310438},
isbn={978-989-758-068-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Incremental Learning Versus Batch Learning for Classification of User’s Behaviour in Medical Imaging
SN - 978-989-758-068-0
AU - Viana-Ferreira C.
AU - Matos S.
AU - Costa C.
PY - 2015
SP - 431
EP - 438
DO - 10.5220/0005219704310438