Interpreting deep learning models for glioma survival classification using visualization and textual explanations

BACKGROUND: Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency map. This...

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Detalles Bibliográficos
Autores principales: Osadebey, Michael, Liu, Qinghui, Fuster-Garcia, Elies, Emblem, Kyrre E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583453/
https://www.ncbi.nlm.nih.gov/pubmed/37853371
http://dx.doi.org/10.1186/s12911-023-02320-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583453/
https://www.ncbi.nlm.nih.gov/pubmed/37853371
http://dx.doi.org/10.1186/s12911-023-02320-2

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