Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.3233/978-1-61499-320-9-283 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Melanoma Cancer Diagnosis,Case-Based Reasoning,Collaborative Systems,Multi-Label,Distance Metric Learning | Oncology,Internal medicine,Computer science,Melanoma | Conference |
Volume | ISSN | Citations |
256 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruben Nicolas | 1 | 4 | 1.74 |
| Albert Fornells | 2 | 118 | 9.27 |
| Elisabet Golobardes | 3 | 206 | 20.16 |
| Guiomar Corral | 4 | 24 | 4.76 |
| Susana Puig | 5 | 5 | 3.84 |
| Joseph Malvehy | 6 | 6 | 1.23 |