Cargando…
MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection
BACKGROUND: Quantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization of prostate cancer. While most cancer detection algorithms utilize either voxel-based or region-...
Autores principales: | Khalvati, Farzad, Zhang, Junjie, Chung, Audrey G., Shafiee, Mohammad Javad, Wong, Alexander, Haider, Masoom A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5956891/ https://www.ncbi.nlm.nih.gov/pubmed/29769042 http://dx.doi.org/10.1186/s12880-018-0258-4 |
Ejemplares similares
-
Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing
por: Dulhanty, Chris, et al.
Publicado: (2020) -
Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models
por: Khalvati, Farzad, et al.
Publicado: (2015) -
Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields
por: Li, Edward, et al.
Publicado: (2016) -
Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer
por: Zhang, Yucheng, et al.
Publicado: (2017) -
Prostate Cancer Detection using Deep Convolutional Neural Networks
por: Yoo, Sunghwan, et al.
Publicado: (2019)