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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.issn0004-8380
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)
dc.languageEnglish
dc.publisherWiley
dc.publisher.urihttps://onlinelibrary.wiley.com/doi/10.1111/ajd.13279
dc.relation.ispartofconferencenameAustralasian College of Dermatologists, 53rd Annual Scientific Meeting
dc.relation.ispartofconferencetitleAustralasian Journal of Dermatology
dc.relation.ispartofdatefrom2021-04-09
dc.relation.ispartofdateto2021-04-11
dc.relation.ispartoflocationVirtual
dc.relation.ispartofpagefrom33
dc.relation.ispartofpageto33
dc.relation.ispartofissueS1
dc.relation.ispartofvolume62
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchcode3202
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsDermatology
dc.titleArtificial intelligence for melanoma diagnosis: from support vector machines to deep learning and beyond
dc.typeConference output
dc.type.descriptionE3 - Conferences (Extract Paper)
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. 33-33
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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