An Automatic Deep Learning–Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study
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Jie Fu | Dan Ruan | Yu Gao | Yingli Yang | Kamal Singhrao | John H Lewis | Xinran Zhong | Sharon Qi | K. Singhrao | D. Ruan | Yingli Yang | Xinran Zhong | Yu Gao | Jie Fu | S. Qi | J. Lewis | Jie Fu
[1] Olivier Gevaert,et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. , 2015, Journal of neuroradiology. Journal de neuroradiologie.
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] Konstantinos Kamnitsas,et al. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.
[4] G. Reifenberger,et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.
[5] Christos Davatzikos,et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.
[6] A. Tamimi,et al. Epidemiology and Outcome of Glioblastoma , 2017 .
[7] D. Rimm,et al. X-Tile , 2004, Clinical Cancer Research.
[8] D. Oldridge,et al. MRI features predict survival and molecular markers in diffuse lower-grade gliomas , 2017, Neuro-oncology.
[9] Thomas Frauenfelder,et al. Influence of inter-observer delineation variability on radiomics stability in different tumor sites , 2018, Acta oncologica.
[10] David Jaffray,et al. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. , 2019, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[13] Andriy Myronenko,et al. 3D MRI brain tumor segmentation using autoencoder regularization , 2018, BrainLes@MICCAI.
[14] Debashis Nandi,et al. A Deep Adaptive Convolutional Network for Brain Tumor Segmentation from Multimodal MR Images , 2019, TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON).
[15] Andrea Vedaldi,et al. Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Hongliang Ren,et al. Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images , 2019, Medical & Biological Engineering & Computing.
[18] Qiang Tian,et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[19] Guangtao Zhai,et al. A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme , 2017, Scientific Reports.
[20] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[21] E. Galanis,et al. What next for newly diagnosed glioblastoma? , 2015, Future oncology.
[22] Natalia Antropova,et al. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets , 2017, Medical physics.
[23] Konstantinos N. Plataniotis,et al. From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and opportunities , 2018, IEEE Signal Processing Magazine.
[24] et al.,et al. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.
[25] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.