Melanoma diagnosis based on collaborative multi-label reasoning

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

doi:10.3233/978-1-61499-320-9-283

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 chapter › Conference contribution › peer-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 language	English
Title of host publication	Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence
Editors	Karina Gibert, Vicent Botti, Ramon Reig-Bolano
Pages	283-292
Number of pages	10
DOIs	https://doi.org/10.3233/978-1-61499-320-9-283
Publication status	Published - 2013

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	256
ISSN (Print)	0922-6389

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.3233/978-1-61499-320-9-283

Cite this

Nicolas, R., Fornells, A., Golobardes, E., Corral, G., Puig, S., & Malvehy, J. (2013). Melanoma diagnosis based on collaborative multi-label reasoning. In K. Gibert, V. Botti, & R. Reig-Bolano (Eds.), Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence (pp. 283-292). (Frontiers in Artificial Intelligence and Applications; Vol. 256). https://doi.org/10.3233/978-1-61499-320-9-283

Nicolas, Ruben ; Fornells, Albert ; Golobardes, Elisabet et al. / Melanoma diagnosis based on collaborative multi-label reasoning. Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence. editor / Karina Gibert ; Vicent Botti ; Ramon Reig-Bolano. 2013. pp. 283-292 (Frontiers in Artificial Intelligence and Applications).

@inproceedings{a70293497d13495396bd557ab202177d,

title = "Melanoma diagnosis based on collaborative multi-label reasoning",

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.",

keywords = "Case-Based Reasoning, Collaborative Systems, Distance Metric Learning, Melanoma Cancer Diagnosis, Multi-Label",

author = "Ruben Nicolas and Albert Fornells and Elisabet Golobardes and Guiomar Corral and Susana Puig and Josep Malvehy",

note = "Funding Information: We thank the Agencia de Gestio d{\textquoteright}Ajuts Universitaris i de Recerca for funding the Grup de Recerca en Sistemes Intel·ligents as recognized group (2009-SGR-183), and for grant 2010FI B2 0084.",

year = "2013",

doi = "10.3233/978-1-61499-320-9-283",

language = "English",

isbn = "9781614993193",

series = "Frontiers in Artificial Intelligence and Applications",

pages = "283--292",

editor = "Karina Gibert and Vicent Botti and Ramon Reig-Bolano",

booktitle = "Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence",

}

Nicolas, R, Fornells, A , Golobardes, E , Corral, G, Puig, S & Malvehy, J 2013, Melanoma diagnosis based on collaborative multi-label reasoning. in K Gibert, V Botti & R Reig-Bolano (eds), Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 256, pp. 283-292. https://doi.org/10.3233/978-1-61499-320-9-283

Melanoma diagnosis based on collaborative multi-label reasoning. / Nicolas, Ruben; Fornells, Albert ; Golobardes, Elisabet et al.
Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence. ed. / Karina Gibert; Vicent Botti; Ramon Reig-Bolano. 2013. p. 283-292 (Frontiers in Artificial Intelligence and Applications; Vol. 256).

Research output: Book chapter › Conference contribution › peer-review

TY - GEN

T1 - Melanoma diagnosis based on collaborative multi-label reasoning

AU - Nicolas, Ruben

AU - Fornells, Albert

AU - Golobardes, Elisabet

AU - Corral, Guiomar

AU - Puig, Susana

AU - Malvehy, Josep

N1 - Funding Information: We thank the Agencia de Gestio d’Ajuts Universitaris i de Recerca for funding the Grup de Recerca en Sistemes Intel·ligents as recognized group (2009-SGR-183), and for grant 2010FI B2 0084.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Case-Based Reasoning

KW - Collaborative Systems

KW - Distance Metric Learning

KW - Melanoma Cancer Diagnosis

KW - Multi-Label

UR - https://www.scopus.com/pages/publications/84894849531

U2 - 10.3233/978-1-61499-320-9-283

DO - 10.3233/978-1-61499-320-9-283

M3 - Conference contribution

AN - SCOPUS:84894849531

SN - 9781614993193

T3 - Frontiers in Artificial Intelligence and Applications

SP - 283

EP - 292

BT - Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence

A2 - Gibert, Karina

A2 - Botti, Vicent

A2 - Reig-Bolano, Ramon

ER -

Nicolas R, Fornells A , Golobardes E , Corral G, Puig S, Malvehy J. Melanoma diagnosis based on collaborative multi-label reasoning. In Gibert K, Botti V, Reig-Bolano R, editors, Artificial Intelligence Research and Development. Proceedings of the 16th International Conference of the Catalan Association for Artificial Intelligence. 2013. p. 283-292. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/978-1-61499-320-9-283

Melanoma diagnosis based on collaborative multi-label reasoning

Abstract

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UN SDGs

Keywords

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