Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery

PURPOSE: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided...

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Detalles Bibliográficos
Autores principales: Jungo, Alain, Doorenbos, Lars, Da Col, Tommaso, Beelen, Maarten, Zinkernagel, Martin, Márquez-Neila, Pablo, Sznitman, Raphael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285003/
https://www.ncbi.nlm.nih.gov/pubmed/37133678
http://dx.doi.org/10.1007/s11548-023-02909-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10285003/
https://www.ncbi.nlm.nih.gov/pubmed/37133678
http://dx.doi.org/10.1007/s11548-023-02909-y

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