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dc.contributor.authorMøllersen, Kajsa
dc.contributor.authorZortea, Maciel
dc.contributor.authorSchopf, Thomas Roger Griesbeck
dc.contributor.authorKirchesch, Herbert M.
dc.contributor.authorGodtliebsen, Fred
dc.date.accessioned2018-01-06T13:55:03Z
dc.date.available2018-01-06T13:55:03Z
dc.date.issued2017-12-21
dc.description.abstractMelanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.en_US
dc.descriptionSource at <a href=https://doi.org/10.1371/journal.pone.0190112> https://doi.org/10.1371/journal.pone.0190112 </a>en_US
dc.identifier.citationMøllersen K, Zortea M, Schopf TR, Kirchesch HM, Godtliebsen F. Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images. PLoS ONE. 2017;12(12)en_US
dc.identifier.cristinIDFRIDAID 1531359
dc.identifier.doi10.1371/journal.pone.0190112
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/10037/11920
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPLoS ONE
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Medisinske Fag: 700::Helsefag: 800::Epidemiologi medisinsk og odontologisk statistikk: 803en_US
dc.subjectVDP::Medical disciplines: 700::Health sciences: 800::Epidemiology medical and dental statistics: 803en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering, visualisering, signalbehandling, bildeanalyse: 429en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation, visualization, signal processing, image processing: 429en_US
dc.titleComparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic imagesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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