A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site and are currently made with biopsy and histology. Here, we develop a deep-learning approach for accurate non-invasive digital histology with whole-brain magnetic resonance imaging (MRI) data. Contrast-enhanc...

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Detalles Bibliográficos
Autores principales: Lyu, Qing, Namjoshi, Sanjeev V., McTyre, Emory, Topaloglu, Umit, Barcus, Richard, Chan, Michael D., Cramer, Christina K., Debinski, Waldemar, Gurcan, Metin N., Lesser, Glenn J., Lin, Hui-Kuan, Munden, Reginald F., Pasche, Boris C., Sai, Kiran K.S., Strowd, Roy E., Tatter, Stephen B., Watabe, Kounosuke, Zhang, Wei, Wang, Ge, Whitlow, Christopher T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676537/
https://www.ncbi.nlm.nih.gov/pubmed/36419451
http://dx.doi.org/10.1016/j.patter.2022.100613

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9676537/
https://www.ncbi.nlm.nih.gov/pubmed/36419451
http://dx.doi.org/10.1016/j.patter.2022.100613

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