MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation

Lana Serrano, Sara; Villena Román, Julio; González Cristóbal, José Carlos y Goñi Menoyo, José Miguel (2007). MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation. En: "8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007", 19/09/2007-21/09/2007, Budapest, Hungria.

Descripción

Título: MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation
Autor/es:
  • Lana Serrano, Sara
  • Villena Román, Julio
  • González Cristóbal, José Carlos
  • Goñi Menoyo, José Miguel
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007
Fechas del Evento: 19/09/2007-21/09/2007
Lugar del Evento: Budapest, Hungria
Título del Libro: Working Notes for the CLEF 2007 Workshop
Fecha: 2007
Materias:
Palabras Clave Informales: Information Retrieval, medical image, image annotation, classification, IRMA code, axis, learning algorithms, nearest-neighbour, machine learning.
Escuela: E.U.I.T. Telecomunicación (UPM) [antigua denominación]
Departamento: Ingeniería y Arquitecturas Telemáticas [hasta 2014]
Grupo Investigación UPM: Grupo de Sistemas Inteligentes
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem, regardless of the domain. FIRE is used as a black-box algorithm to extract different groups of image features that are later used for training different classifiers in order to predict the IRMA code. Three types of classifiers are built. The first type is a single classifier that predicts the complete IRMA code. The second type is a two level classifier composed of four classifiers that individually predict each axis of the IRMA code. The third type is similar to the second one but predicts a combined pair of axes. The main idea behind the definition of our experiments is to evaluate whether an axis-by-axis prediction is better than a prediction by pairs of axes or the complete code, or vice versa. We submitted 30 experiments to be evaluated and results are disappointing compared to other groups. However, the main conclusion that can be drawn from the experiments is that, irrespective of the selected image features, the axis-by-axis prediction achieves more accurate results not only than the prediction of a combined pair of axes but also, in turn, than the prediction of the complete IRMA code. In addition, data normalization seems to improve the predictions and vector-based features are preferred over histogram-based ones.

Más información

ID de Registro: 4685
Identificador DC: http://oa.upm.es/4685/
Identificador OAI: oai:oa.upm.es:4685
Depositado por: Memoria Investigacion
Depositado el: 22 Oct 2010 10:19
Ultima Modificación: 20 Abr 2016 13:48
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