Quantifying acceptable artefact ranges for dermatologic classification algorithms

BACKGROUND: Many classifiers have been developed that can distinguish different types of skin lesions (e.g., benign nevi, melanoma) with varying degrees of success.(1–5) However, even successfully trained classifiers may perform poorly on images that include artefacts. While problems created by hair...

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Detalles Bibliográficos
Autores principales: Petrie, T.C., Larson, C., Heath, M., Samatham, R., Davis, A., Berry, E.G., Leachman, S.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060017/
https://www.ncbi.nlm.nih.gov/pubmed/35664971
http://dx.doi.org/10.1002/ski2.19

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9060017/
https://www.ncbi.nlm.nih.gov/pubmed/35664971
http://dx.doi.org/10.1002/ski2.19

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