Bagnasco, Stefano and Bottigli, Ubaldo and Cerello, Piergiorgio and Cheran, Sorin Cristian and Delogu, Pasquale and Fantacci, Maria Evelina and Fauci, Francesco and Forni, G. and Lauria, Adele and Lopez Torres, Ernesto and Magro, Rosario and Masala, Giovanni Luca Christian and Oliva, Piernicola and Palmiero, Rosa and Ramello, Luciano and Raso, Giuseppe and Retico, Alessandra and Sitta, M. and Stumbo, Simone and Tangaro, Sonia and Zanon, Eugenio (2005) GPCALMA: a grid-based tool for mammographic screening. Methods of Information in Medicine, Vol. 44 (2), p. 244-248. ISSN 0026-1270. Article.
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The next generation of High Energy Physics (HEP) experiments requires a GRID approach to a distributed computing system and the associated data management: the key concept is the Virtual Organisation (VO), a group of distributed users with a common goal and the will to share their resources. A similar approach is being applied to a group of Hospitals which joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), which will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. HEP techniques come into play in writing the application code, which makes use of neural networks for the image analysis and proved to be useful in improving the radiologists' performances in the diagnosis. GRID technologies allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated to screening programs. A prototype of the system, based on AliEn GRID Services , is already available, with a central Server running common services  and several clients connecting to it. Mammograms can be acquired in any location; the related information required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. The result of a query can be used as input for analysis algorithms, which are executed on nodes that are in general remote to the user (but always local to the input images) thanks to the PROOF facility , a set of C++ classes that provide the functionality required to configure several distributed nodes in a way that allows parallel analysis of similar data samples. The selected approach avoids data transfers for all the images with a negative diagnosis (about 95% of the sample) and allows an almost real time diagnosis for the 5% of images with high cancer probability.
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