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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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Quantitative evaluation of MRI and histological characteristics of the 5xFAD Alzheimer mouse brain., , , , , and . NeuroImage, (2013)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)Supervised Learning based Multimodal MRI Brain Tumour Segmentation using Texture Features from Supervoxels#., , , , , , , and . Comput. Methods Programs Biomed., (2018)A Prototype Decision Support System for MR Spectroscopy-Assisted Diagnosis of Brain Tumours., , , , , , , and . MedInfo, volume 84 of Studies in Health Technology and Informatics, page 561-565. IOS Press, (2001)Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique., , , , , , , , , and . NeuroImage, (2020)Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy., , , , and . IEEE Trans. Biomed. Eng., 62 (12): 2860-2866 (2015)Brain tumor classification based on long echo proton MRS signals., , , , , , , , , and 1 other author(s). Artif. Intell. Medicine, 31 (1): 73-89 (2004)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)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)