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

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

Description

Title: MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation
Author/s:
  • Lana Serrano, Sara
  • Villena Román, Julio
  • González Cristóbal, José Carlos
  • Goñi Menoyo, José Miguel
Item Type: Presentation at Congress or Conference (Article)
Event Title: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007
Event Dates: 19/09/2007-21/09/2007
Event Location: Budapest, Hungria
Title of Book: Working Notes for the CLEF 2007 Workshop
Date: 2007
Subjects:
Freetext Keywords: Information Retrieval, medical image, image annotation, classification, IRMA code, axis, learning algorithms, nearest-neighbour, machine learning.
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Ingeniería y Arquitecturas Telemáticas [hasta 2014]
UPM's Research Group: Grupo de Sistemas Inteligentes
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 4685
DC Identifier: http://oa.upm.es/4685/
OAI Identifier: oai:oa.upm.es:4685
Official URL: http://www.clef-campaign.org/2007/working_notes/lanaCLEF2007Annotation.pdf
Deposited by: Memoria Investigacion
Deposited on: 22 Oct 2010 10:19
Last Modified: 20 Apr 2016 13:48
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