Cargando…
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...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
|
| Materias: | |
| 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 |