Pattern recognition for cache management in distributed medical imaging environments
- 185 Downloads
Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon.
This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated.
The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75 % of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism.
The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.
KeywordsPattern recognition PACS Neural network Adaptive model Cache Prefetching
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF Grant No. 115372). Carlos Viana-Ferreira is funded by the FCT Grant SFRH/BD/68280/2010. Sérgio Matos is funded under the FCT Investigator programme.
Compliance with ethical standards
Conflict of interest
Carlos Viana-Ferreira, Luís Ribeiro, Sérgio Matos and Carlos Costa declare that they have no conflict of interest.
- 5.Younis MYA, Kifayat K (2013) Secure cloud computing for critical infrastructure: A survey. Liverpool John Moores University, United Kingdom, Tech RepGoogle Scholar
- 6.Viana-Ferreira C, Matos S, Costa C (2015) Incremental learning versus batch learning for classification of user’s behaviour in medical imaging. Paper presented at the 8th international conference on health informatics, Lisbon, Portugal, January 2015Google Scholar
- 14.Huang H (2011) PACS and imaging informatics: basic principles and applications. Wiley-Blackwell, HobokenGoogle Scholar
- 16.Yu W, Oral HS, Canon RS, Vetter JS, Sankaran R (2008) Empirical analysis of a large-scale hierarchical storage system. Euro-Par 2008 Parallel Process pp 130–140Google Scholar
- 17.Yalamanchili C, Vijayasankar K, Zadok E, Sivathanu G (2009) DHIS: discriminating hierarchical storage. In: Proceedings of SYSTOR 2009: the Israeli experimental systems conference. ACM, p 9Google Scholar
- 18.Doganata YN, Tantawi AN (1994) A cost/performance study of video servers with hierarchical storage. In: Multimedia computing and systems, 1994., Proceedings of the international conference on, 1994. IEEE, pp 393–402Google Scholar
- 20.Pal MB, Jain DC (2014) An approach for web pre-fetching to enhance user interaction of web application using Markov model. In: Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on, 7–9 April 2014. pp 373–377. doi: 10.1109/CSNT.2014.80
- 26.Silva LAB, Costa C, Oliveira JL (2013) An agile framework to support distributed medical imaging scenarios. Paper presented at the IEEE international conference on healthcare informatics 2013 (ICHI 2013), Philadelphia, USAGoogle Scholar
- 27.Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, HobokenGoogle Scholar
- 28.Michalewicz Z, Schmidt M, Michalewicz M, Chiriac C (2006) Adaptive business intelligence. Springer, BerlinGoogle Scholar
- 29.Campos SC, Costa C, Silva LAB (2012) A network sensor for medical imaging workflows. In: Information Systems and Technologies (CISTI), 2012 7th Iberian Conference on, 20–23 June 2012. pp 1–6Google Scholar
- 31.Meyer-Baese A, Schmid VJ (2014) Pattern recognition and signal analysis in medical imaging. Elsevier, AmsterdamGoogle Scholar
- 32.Basu JK, Bhattacharyya D, Kim TH (2010) Use of artificial neural network in pattern recognition. Int J Softw Eng Appl 4(2):23–33Google Scholar
- 33.Viana-Ferreira C, Costa C (2014) DICOM traffic generator based on behavior profiles. Paper presented at the IEEE-EMBS International Conferences on Biomedical and Health Informatics, Valencia, SpainGoogle Scholar