Melanoma diagnosis based on collaborative multi-label reasoning

Ruben Nicolas*, Albert Fornells, Elisabet Golobardes, Guiomar Corral, Susana Puig, Josep Malvehy

*Corresponding author for this work

Research output: Book chapterConference contributionpeer-review

Abstract

The number of melanoma cancer-related death has increased over the last few years due to the new solar habits. Early diagnosis has become the best prevention method. This work presents a melanoma diagnosis architecture based on the collaboration of several multi-label case-based reasoning subsystems called DERMA. The system has to face several challenges that include data characterization, pattern matching, reliable diagnosis and self-explanation capabilities. Experiments using two subsystems specialized in confocal and dermoscopy data from images respectively have provided promising results to help experts assess melanoma patterns.

Original languageEnglish
Title of host publicationArtificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence
EditorsKarina Gibert, Vicent Botti, Ramon Reig-Bolano
Pages283-292
Number of pages10
DOIs
Publication statusPublished - 2013

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume256
ISSN (Print)0922-6389

Keywords

  • Case-Based Reasoning
  • Collaborative Systems
  • Distance Metric Learning
  • Melanoma Cancer Diagnosis
  • Multi-Label

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