Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer

Purpose: Integrative analysis combining diagnostic imaging and genomic information can uncover biological insights into lesions that are visible on radiologic images. We investigate techniques for interrogating a deep neural network trained to predict quantitative image (radiomic) features and histo...

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Detalles Bibliográficos
Autores principales: Smedley, Nova F., Aberle, Denise R., Hsu, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2021
Materias:
Special Section on Radiogenomics in Prognosis and Treatment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105647/
https://www.ncbi.nlm.nih.gov/pubmed/33977113
http://dx.doi.org/10.1117/1.JMI.8.3.031906

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105647/
https://www.ncbi.nlm.nih.gov/pubmed/33977113
http://dx.doi.org/10.1117/1.JMI.8.3.031906

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