Limitations of Out-of-Distribution Detection in 3D Medical Image Segmentation

Deep learning models perform unreliably when the data come from a distribution different from the training one. In critical applications such as medical imaging, out-of-distribution (OOD) detection methods help to identify such data samples, preventing erroneous predictions. In this paper, we furthe...

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Detalles Bibliográficos
Autores principales: Vasiliuk, Anton, Frolova, Daria, Belyaev, Mikhail, Shirokikh, Boris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10532230/
https://www.ncbi.nlm.nih.gov/pubmed/37754955
http://dx.doi.org/10.3390/jimaging9090191

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10532230/
https://www.ncbi.nlm.nih.gov/pubmed/37754955
http://dx.doi.org/10.3390/jimaging9090191

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