Use this identifier to quote or link this document: http://hdl.handle.net/2072/450192

Title:	Applying Distance Metric Learning in a Collaborative Melanoma Diagnosis System with Case-Based Reasoning
Author:	Nicolàs Sans, Rubén; Vernet Bellet, David; Golobardes, Elisabet; Fornells Herrera, Albert; Torre Frade, Fernando de la; Puig, Susana
Other authors:	Universitat Ramon Llull. La Salle; Carnegie Mellon University; Hospital Clinic i Provincial de Barcelona
Abstract:	Current social habits in solar exposure have increased the appearance of melanoma cancer in the last few years. The highest mortality rates in dermatological cancers are caused for this illness. In spite of it, recent studies demonstrate that early diagnosis increases life expectancy. This work introduces a way to classify dermatological cancer with highest rates of accuracy, specificity and sensitivity. The approach is the result of the improvement of previous works that combine information of two of the most important non-invasive image techniques: Reflectance Confocal Microscopy and Dermatoscopy. Current work achieve better results than the previous systems by the use of Distance Metric Learning to the different Case Memories.
Publication date:	2011-12
Subject (UDC):	616.5 - Pell. Dermatologia clínica 62 - Enginyeria. Tecnologia
Subject(s):	Melanoma Dermatologia Medicina -- Innovacions
Rights:	© L'autor/a. Tots el drets reservats
Pages:	9 p.
Document type:	Conference Object
Published by:	The 14th Workshop on Case-based reasoning at the 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, 13-15 of December 2011
Publish at:	Proceedings of the 14th Workshop on Case-based reasoning at the 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
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