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MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks., , , , , and . BrainLes@MICCAI, volume 10670 of Lecture Notes in Computer Science, page 204-215. Springer, (2017)Supervised learning-based multimodal MRI brain image analysis.. University of Lincoln, UK, (2017)British Library, EThOS.A Hybrid Method for Haemorrhage Segmentation in Trauma Brain CT., , , , and . MIUA, page 99-104. BMVA, (2014)Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels#., , , , , , , and . Comput. Methods Programs Biomed., (2018)Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks., , , , and . IEEE Trans. Image Process., (2020)MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks., , , , , and . CoRR, (2019)QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results., , , , , , , , , and 82 other author(s). CoRR, (2021)Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI., , , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 12 (2): 183-203 (2017)Brain Tumour Grading in Different MRI Protocols using SVM on Statistical Features., , , , and . MIUA, page 259-264. BMVA, (2014)Multi-resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction., and . BrainLes@MICCAI (1), volume 11992 of Lecture Notes in Computer Science, page 221-230. Springer, (2019)