A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

Carlos Sáez; Juan Miguel García-Gómez; Javier Vicente; Salvador Tortajada; Jan Luts; David Dupplaw; Sabine Van Huffel; Montserrat Robles; Carlos Sáez; Juan Miguel García-Gómez; Javier Vicente; Salvador Tortajada; Jan Luts; David Dupplaw; Sabine Van Huffel; Montserrat Robles

doi:10.1017/S0269888911000129

Abstract: New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politécnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

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A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

Grupo de Informática Biomédica

Department of Electrical Engineering (ESAT)

Intelligence

Corresponding authors: Carlos Sáez ; Juan Miguel García-Gómez ; Javier Vicente ; Salvador Tortajada ; Jan Luts ; David Dupplaw ; Sabine Van Huffel ; Montserrat Robles

Published online: 28 July 2011

Abstract: Abstract: New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language1 classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politécnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

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Acknowledgments

This work was partially funded by the European Commission: HealthAgents (contract no. FP6-2005-IST 027214). Jan Luts is a PhD student supported by an IWT grant. Carlos Sáez, Salvador Tortajada and Javier Vicente are PhD students partially supported by the Programa Torres Quevedo from the Ministerio de Educación y Ciencia, co-founded by the European Social Fund (PTQ-08-01-06817, PTQ-08-01-06802, PTQ05-02-03386). We thank INTERPRET partners for their support and for providing the data used for training some of the classifiers included in the HealthAgents network; in particular we thank C. Majós (IDI-Bellvitge), John Griffiths (SGUL), Arend Heerschap (RU), Witold Gajewicz (MUL), Jorge Calvar (FLENI), Margarida Julià-Sapé (UAB) and Carles Arús (UAB). The language revision of this document was funded by the Universidad Politécnica de Valencia. This work has been partially supported by the Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001.

Extensible Markup Language (XML), http://www.w3.org/XML/

http://www.healthagents.net/

http://www.etumour.net/

http://www.esat.kuleuven.be/

http://www.ibime.upv.es/bmg/

Available from http://azizu.uab.es/interpret/mrsdata/mrsdata.html

JADE, Telecom Italia, http://jade.tilab.com/

FIPA, IEEE Computer Society, http://www.fipa.org/

RDF, http://www.w3.org/RDF/

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Carlos Sáez, Juan Miguel García-Gómez, Javier Vicente, Salvador Tortajada, Jan Luts, David Dupplaw, Sabine Van Huffel, Montserrat Robles. 2011. A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents. The Knowledge Engineering Review. 26: doi: 10.1017/S0269888911000129

Carlos Sáez, Juan Miguel García-Gómez, Javier Vicente, Salvador Tortajada, Jan Luts, David Dupplaw, Sabine Van Huffel, Montserrat Robles. 2011. A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents. The Knowledge Engineering Review. 26: doi: 10.1017/S0269888911000129

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A generic and extensible automatic classification framework applied to brain tumour diagnosis in HealthAgents

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