Ranking of brain tumour classifiers using a bayesian approach

Javier Vicente; Juan Miguel García-Gómez; Salvador Tortajada; Alfredo T. Navarro; Franklyn A. Howe; Andrew C. Peet; Margarida Julià-Sapé; Bernardo Celda; Pieter Wesseling; Magí Lluch-Ariet; Montserrat Robles

doi:10.1007/978-3-642-02478-8_126

Ranking of brain tumour classifiers using a bayesian approach

Javier Vicente, Juan Miguel García-Gómez, Salvador Tortajada, Alfredo T. Navarro, Franklyn A. Howe, Andrew C. Peet, Margarida Julià-Sapé, Bernardo Celda, Pieter Wesseling, Magí Lluch-Ariet, Montserrat Robles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This study presents a ranking for classifers using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with ¹H magnetic resonance spectroscopy (MRS) signals following a multiproject multicenter evaluation approach. We demonstrate that such a framework can be effectively applied to the real problem of selecting classifiers for brain tumour classification.

Original language	English
Title of host publication	Bio-Inspired Systems
Subtitle of host publication	Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings
Pages	1005-1012
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-02478-8_126
Publication status	Published - 2009
Externally published	Yes
Event	10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain Duration: 10 Jun 2009 → 12 Jun 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5517 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Work-Conference on Artificial Neural Networks, IWANN 2009
Country/Territory	Spain
City	Salamanca
Period	10/06/09 → 12/06/09

Access to Document

10.1007/978-3-642-02478-8_126

Cite this

Vicente, J., García-Gómez, J. M., Tortajada, S., Navarro, A. T., Howe, F. A., Peet, A. C., Julià-Sapé, M., Celda, B., Wesseling, P., Lluch-Ariet, M., & Robles, M. (2009). Ranking of brain tumour classifiers using a bayesian approach. In Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings (PART 1 ed., pp. 1005-1012). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-02478-8_126

Vicente, Javier ; García-Gómez, Juan Miguel ; Tortajada, Salvador et al. / Ranking of brain tumour classifiers using a bayesian approach. Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1. ed. 2009. pp. 1005-1012 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{7c04d2ad92a042238e787156de9be9b6,

title = "Ranking of brain tumour classifiers using a bayesian approach",

abstract = "This study presents a ranking for classifers using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with 1H magnetic resonance spectroscopy (MRS) signals following a multiproject multicenter evaluation approach. We demonstrate that such a framework can be effectively applied to the real problem of selecting classifiers for brain tumour classification.",

author = "Javier Vicente and Garc{\'i}a-G{\'o}mez, {Juan Miguel} and Salvador Tortajada and Navarro, {Alfredo T.} and Howe, {Franklyn A.} and Peet, {Andrew C.} and Margarida Juli{\`a}-Sap{\'e} and Bernardo Celda and Pieter Wesseling and Mag{\'i} Lluch-Ariet and Montserrat Robles",

year = "2009",

doi = "10.1007/978-3-642-02478-8_126",

language = "English",

isbn = "3642024777",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "1005--1012",

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note = "10th International Work-Conference on Artificial Neural Networks, IWANN 2009 ; Conference date: 10-06-2009 Through 12-06-2009",

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Vicente, J, García-Gómez, JM, Tortajada, S, Navarro, AT, Howe, FA, Peet, AC, Julià-Sapé, M, Celda, B, Wesseling, P, Lluch-Ariet, M & Robles, M 2009, Ranking of brain tumour classifiers using a bayesian approach. in Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5517 LNCS, pp. 1005-1012, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, 10/06/09. https://doi.org/10.1007/978-3-642-02478-8_126

Ranking of brain tumour classifiers using a bayesian approach. / Vicente, Javier; García-Gómez, Juan Miguel; Tortajada, Salvador et al.
Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1. ed. 2009. p. 1005-1012 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5517 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Vicente, Javier

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AU - Howe, Franklyn A.

AU - Peet, Andrew C.

AU - Julià-Sapé, Margarida

AU - Celda, Bernardo

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AU - Lluch-Ariet, Magí

AU - Robles, Montserrat

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AB - This study presents a ranking for classifers using a Bayesian perspective. This ranking framework is able to evaluate the performance of the models to be compared when they are inferred from different sets of data. It also takes into account the performance obtained on samples not used during the training of the classifiers. Besides, this ranking assigns a prior to each model based on a measure of similarity of the training data to a test case. An evaluation consisting of ranking brain tumour classifiers is presented. These multilayer perceptron classifiers are trained with 1H magnetic resonance spectroscopy (MRS) signals following a multiproject multicenter evaluation approach. We demonstrate that such a framework can be effectively applied to the real problem of selecting classifiers for brain tumour classification.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Vicente J, García-Gómez JM, Tortajada S, Navarro AT, Howe FA, Peet AC et al. Ranking of brain tumour classifiers using a bayesian approach. In Bio-Inspired Systems: Computational and Ambient Intelligence - 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. PART 1 ed. 2009. p. 1005-1012. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-02478-8_126

Ranking of brain tumour classifiers using a bayesian approach

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