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dc.contributor.authorByth, LA
dc.contributor.authorByth, JL
dc.date.accessioned2021-06-14T23:09:33Z
dc.date.available2021-06-14T23:09:33Z
dc.date.issued2021
dc.identifier.issn0004-8380en_US
dc.identifier.urihttp://hdl.handle.net/10072/405127
dc.description.abstractMelanoma diagnosis is a vexing problem, with practitioners requiring years of training and experience to achieve proficiency. Machine learning represents a disruptive force in this and other disciplines that rely on visual pattern recognition. Transfer learning using convolutional neural networks represents the state-of-the-art in artificial melanoma diagnosis, achieving similar sensitivity and specificity to experts. (1) Several machine learning algorithms, from support vector machines to neural networks, have also been applied to decision support which can guide biopsy decision-making by general practitioners and inexperienced clinicians. (2) This presentation outlines the spectrum of artificial intelligence technologies which have shown promise for melanoma diagnosis and decision support, including recent results using deep learning to predict the need for lesion excision. This is followed by a review of current challenges facing the use of machine learning in clinical practice. Inherent difficulties include shortcomings in training datasets, risks of overfitting and poor inductive biases. There are also broader issues with model interpretability and an uncertain regulatory environment which must be resolved. (3)en_US
dc.languageEnglishen_US
dc.publisherWileyen_US
dc.publisher.urihttps://onlinelibrary.wiley.com/doi/10.1111/ajd.13279en_US
dc.relation.ispartofconferencenameAustralasian College of Dermatologists, 53rd Annual Scientific Meetingen_US
dc.relation.ispartofconferencetitleAustralasian Journal of Dermatologyen_US
dc.relation.ispartofdatefrom2021-04-09
dc.relation.ispartofdateto2021-04-11
dc.relation.ispartoflocationVirtualen_US
dc.relation.ispartofpagefrom33en_US
dc.relation.ispartofpageto33en_US
dc.relation.ispartofissueS1en_US
dc.relation.ispartofvolume62en_US
dc.subject.fieldofresearchClinical Sciencesen_US
dc.subject.fieldofresearchPaediatrics and Reproductive Medicineen_US
dc.subject.fieldofresearchcode1103en_US
dc.subject.fieldofresearchcode1114en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsLife Sciences & Biomedicineen_US
dc.subject.keywordsDermatologyen_US
dc.titleArtificial intelligence for melanoma diagnosis: from support vector machines to deep learning and beyonden_US
dc.typeConference outputen_US
dc.type.descriptionE3 - Conferences (Extract Paper)en_US
dcterms.bibliographicCitationByth, LA; Byth, JL, Artificial intelligence for melanoma diagnosis: from support vector machines to deep learning and beyond, Australasian Journal of Dermatology, 2021, 62 (S1), pp. 33-33en_US
dc.date.updated2021-06-13T22:21:10Z
gro.hasfulltextNo Full Text
gro.griffith.authorByth, Lachlan A.


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    Contains papers delivered by Griffith authors at national and international conferences.

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