Cargando…
Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features
Quantitative features are generated from a tumor phenotype by various data characterization, feature-extraction approaches and have been used successfully as a biomarker. These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and te...
Autores principales: | Paul, Rahul, Schabath, Matthew, Balagurunathan, Yoganand, Liu, Ying, Li, Qian, Gillies, Robert, Hall, Lawrence O., Goldgof, Dmitry B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Grapho Publications, LLC
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6403047/ https://www.ncbi.nlm.nih.gov/pubmed/30854457 http://dx.doi.org/10.18383/j.tom.2018.00034 |
Ejemplares similares
-
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma
por: Paul, Rahul, et al.
Publicado: (2016) -
Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules
por: Balagurunathan, Yoganand, et al.
Publicado: (2019) -
Delta radiomic features improve prediction for lung cancer incidence: A nested case–control analysis of the National Lung Screening Trial
por: Cherezov, Dmitry, et al.
Publicado: (2018) -
Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening
por: Pérez-Morales, Jaileene, et al.
Publicado: (2020) -
Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
por: Yip, Stephen S. F., et al.
Publicado: (2017)