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Pattern recognition for cache management in distributed medical imaging environments

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Electronics, Telecommunications and Informatics and Institute of Electronics and Telematics Engineering of Aveiro, University of Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    Carlos Viana-Ferreira, Luís Ribeiro, Sérgio Matos & Carlos Costa

Authors
  1. Carlos Viana-Ferreira
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  2. Luís Ribeiro
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  3. Sérgio Matos
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  4. Carlos Costa
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Corresponding author

Correspondence to Carlos Viana-Ferreira.

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Conflict of interest

Carlos Viana-Ferreira, Luís Ribeiro, Sérgio Matos and Carlos Costa declare that they have no conflict of interest.

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Cite this article

Viana-Ferreira, C., Ribeiro, L., Matos, S. et al. Pattern recognition for cache management in distributed medical imaging environments. Int J CARS 11, 327–336 (2016). https://doi.org/10.1007/s11548-015-1272-4

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  • DOI: https://doi.org/10.1007/s11548-015-1272-4

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