A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI
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DOI: 10.1371/journal.pone.0095753
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References listed on IDEAS
- Bordier, Cécile & Dojat, Michel & Micheaux, Pierre Lafaye de, 2011. "Temporal and Spatial Independent Component Analysis for fMRI Data Sets Embedded in the AnalyzeFMRI R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i09).
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- Fahimeh Hadavimoghaddam & Mehdi Ostadhassan & Ehsan Heidaryan & Mohammad Ali Sadri & Inna Chapanova & Evgeny Popov & Alexey Cheremisin & Saeed Rafieepour, 2021. "Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations," Energies, MDPI, vol. 14(4), pages 1-16, February.
- Raymond Salvador & Joaquim Radua & Erick J Canales-Rodríguez & Aleix Solanes & Salvador Sarró & José M Goikolea & Alicia Valiente & Gemma C Monté & María del Carmen Natividad & Amalia Guerrero-Pedraza, 2017. "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
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