Aug 7, 2018 · For example, neural networks was able to predict on average 8.5% closer to each patient's measured enhancing ROI's major axis length than linear ...
We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A ...
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(PDF) Using deep neural networks for radiogenomic analysis
www.researchgate.net › publication › 32...
We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A ...
Smedley NF, Hsu W. Using deep neural networks for radiogenomic analysis. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1529-1533.
Oct 7, 2021 · We leave survival analysis using the radiogenomic associations found in our neural network models as future work. We further explored the ...
Deep learning techniques are applied to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients ...
Apr 15, 2022 · This study demonstrates the application of deep neural networks and radiomics-based machine learning models to predict relapse in MCL on ...
Oct 26, 2023 · This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis.
This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research ...
Nov 12, 2021 · Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues.