Bringing the algorithms to the data: cloud---based benchmarking for medical image analysis

Authors:
Allan Hanbury;Henning Müller;Georg Langs;Marc André Weber;Bjoern H. Menze;Tomas Salas Fernandez
Affiliations:
Vienna University of Technology, Austria;University of Applied Sciences Western Switzerland (HES---SO), Switzerland;CIR, Dep. of Radiology, Medical University of Vienna, Austria;Radiology Department, University of Heidelberg, Germany;ETHZ, Zürich, Switzerland;Gencat, Spain
Venue:
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Year:
2012

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Abstract

Benchmarks have shown to be an important tool to advance science in the fields of information analysis and retrieval. Problems of running benchmarks include obtaining large amounts of data, annotating it and then distributing it to the participants of a benchmark. Distribution of the data to participants is currently mostly done via data download that can take hours for large data sets and in countries with slow Internet connections even days. Sending physical hard disks was also used for distributing very large scale data sets (for example by TRECvid) but also this becomes infeasible if the data sets reach sizes of 5---10 TB. With cloud computing it is possible to make very large data sets available in a central place with limited costs. Instead of distributing the data to the participants, the participants can compute their algorithms on virtual machines of the cloud providers. This text presents reflections and ideas of a concrete project on using cloud---based benchmarking paradigms for medical image analysis and retrieval. It is planned to run two evaluation campaigns in 2013 and 2014 using the proposed technology.