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Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?

, , , and . ISBI, page 1039-1042. IEEE, (2014)

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Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels#., , , , , , , and . Computer Methods and Programs in Biomedicine, (2018)Ranking of Brain Tumour Classifiers Using a Bayesian Approach., , , , , , , , , and 1 other author(s). IWANN (1), volume 5517 of Lecture Notes in Computer Science, page 1005-1012. Springer, (2009)Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?, , , and . ISBI, page 1039-1042. IEEE, (2014)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 tumor classification based on long echo proton MRS signals., , , , , , , , , and 1 other author(s). Artificial Intelligence in Medicine, 31 (1): 73-89 (2004)Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy., , , , and . IEEE Trans. Biomed. Engineering, 62 (12): 2860-2866 (2015)Unsupervised Feature Extraction of in vivo Magnetic Resonance Spectra of Brain Tumours Using Independent Component Analysis., , , and . IDEAL, volume 2412 of Lecture Notes in Computer Science, page 441-446. Springer, (2002)Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis., , , , , , , , , and 8 other author(s). Journal of Biomedical Informatics, 44 (4): 677-687 (2011)