Cargando…
Deep learning classification of lung cancer histology using CT images
Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic dat...
Autores principales: | Chaunzwa, Tafadzwa L., Hosny, Ahmed, Xu, Yiwen, Shafer, Andrea, Diao, Nancy, Lanuti, Michael, Christiani, David C., Mak, Raymond H., Aerts, Hugo J. W. L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7943565/ https://www.ncbi.nlm.nih.gov/pubmed/33727623 http://dx.doi.org/10.1038/s41598-021-84630-x |
Ejemplares similares
-
Foundation Models for Quantitative Biomarker Discovery in Cancer Imaging
por: Pai, Suraj, et al.
Publicado: (2023) -
A Career in Evolutionary Biology and Elucidating the Genetic Basis of Variation: An Interview with Hopi Hoekstra, PhD
por: Chaunzwa, Tafadzwa L.
Publicado: (2018) -
Deep learning to estimate lung disease mortality from chest radiographs
por: Weiss, Jakob, et al.
Publicado: (2023) -
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
por: Hosny, Ahmed, et al.
Publicado: (2018) -
Deep Learning to Assess Long-term Mortality From Chest Radiographs
por: Lu, Michael T., et al.
Publicado: (2019)