Cargando…

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ortega-Martorell, Sandra, Ruiz, Héctor, Vellido, Alfredo, Olier, Iván, Romero, Enrique, Julià-Sapé, Margarida, Martín, José D., Jarman, Ian H., Arús, Carles, Lisboa, Paulo J. G.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2013
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871596/ https://www.ncbi.nlm.nih.gov/pubmed/24376744 http://dx.doi.org/10.1371/journal.pone.0083773

Ejemplares similares

Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours
por: Ortega-Martorell, Sandra, et al.
Publicado: (2012)

SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system
por: Ortega-Martorell, Sandra, et al.
Publicado: (2010)

Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data
por: Ortega-Martorell, Sandra, et al.
Publicado: (2012)

From raw data to data-analysis for magnetic resonance spectroscopy – the missing link: jMRUI2XML
por: Mocioiu, Victor, et al.
Publicado: (2015)

Unraveling response to temozolomide in preclinical GL261 glioblastoma with MRI/MRSI using radiomics and signal source extraction
por: Núñez, Luis Miguel, et al.
Publicado: (2020)

Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
por: Ungan, Gülnur, et al.
Publicado: (2023)

The INTERPRET Decision-Support System version 3.0 for evaluation of Magnetic Resonance Spectroscopy data from human brain tumours and other abnormal brain masses
por: Pérez-Ruiz, Alexander, et al.
Publicado: (2010)

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models
por: Ortega-Martorell, Sandra, et al.
Publicado: (2019)

Brain metabolic pattern analysis using a magnetic resonance spectra classification software in experimental stroke
por: Jiménez-Xarrié, Elena, et al.
Publicado: (2017)

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites
por: Casaña-Eslava, Raúl Vicente, et al.
Publicado: (2020)

Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI
por: Simões, Rui V, et al.
Publicado: (2010)

Explaining the Neural Network: A Case Study to Model the Incidence of Cervical Cancer
por: Lisboa, Paulo J. G., et al.
Publicado: (2020)

A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway
por: Shim, Jaeseok, et al.
Publicado: (2023)

MRI/MRS in neuroinflammation: methodology and applications
por: Quarantelli, Mario
Publicado: (2015)

Enhanced survival prediction using explainable artificial intelligence in heart transplantation
por: Lisboa, Paulo J. G., et al.
Publicado: (2022)

Metabolomics of Therapy Response in Preclinical Glioblastoma: A Multi-Slice MRSI-Based Volumetric Analysis for Noninvasive Assessment of Temozolomide Treatment
por: Arias-Ramos, Nuria, et al.
Publicado: (2017)

Semi-supervised learning /
Publicado: (2010)

Generative Adversarial Training for Supervised and Semi-supervised Learning
por: Wang, Xianmin, et al.
Publicado: (2022)

The Menopause Rating Scale (MRS) scale: A methodological review
por: Heinemann, Klaas, et al.
Publicado: (2004)

Bootstrapping semi-supervised annotation method for potential suicidal messages
por: Acuña Caicedo, Roberto Wellington, et al.
Publicado: (2022)

Supervised, semi-supervised and unsupervised inference of gene regulatory networks
por: Maetschke, Stefan R., et al.
Publicado: (2014)

Self-supervised Learning for Semi-supervised Time Series Classification
por: Jawed, Shayan, et al.
Publicado: (2020)

A Semi-Supervised Methodology for Fishing Activity Detection Using the Geometry behind the Trajectory of Multiple Vessels
por: Ferreira, Martha Dais, et al.
Publicado: (2022)

Semi-supervised discovery of differential genes
por: Oba, Shigeyuki, et al.
Publicado: (2006)

Semi-supervised protein subcellular localization
por: Xu, Qian, et al.
Publicado: (2009)

Semi-supervised classifier guided by discriminator
por: Jamroziński, Sebastian, et al.
Publicado: (2022)

Semi-supervised learning in cancer diagnostics
por: Eckardt, Jan-Niklas, et al.
Publicado: (2022)

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks
por: Ortega-Martorell, Sandra, et al.
Publicado: (2022)

Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs
por: Schwarz, Andreas, et al.
Publicado: (2019)

Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer
por: Al-Azzam, Nosayba, et al.
Publicado: (2021)

Mrs Arley, a man and Mrs Bjerga
por: Unknown
Publicado: (2002)

Protein Ranking by Semi-Supervised Network Propagation
por: Weston, Jason, et al.
Publicado: (2006)

Semi-Supervised Morphosyntactic Classification of Old Icelandic
por: Urban, Kryztof, et al.
Publicado: (2014)

Semi-supervised method for biomedical event extraction
por: Wang, Jian, et al.
Publicado: (2013)

Semi-Supervised Fuzzy Clustering with Feature Discrimination
por: Li, Longlong, et al.
Publicado: (2015)

Variational Information Bottleneck for Semi-Supervised Classification
por: Voloshynovskiy, Slava, et al.
Publicado: (2020)

Improved semi-supervised autoencoder for deception detection
por: Fu, Hongliang, et al.
Publicado: (2019)

Simple strategies for semi-supervised feature selection
por: Sechidis, Konstantinos, et al.
Publicado: (2017)

Semi-Supervised Training for Positioning of Welding Seams
por: Zhang, Wenbin, et al.
Publicado: (2021)

Semi-Supervised Clustering for Financial Risk Analysis
por: Han, Yihan, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_1ij5ahfu4v16v4if0lpib7c8tk