Machine Learning in NeuroImaging for various tasks, e.g., regression, classification and clustering.
Project description
MLNI
Machine Learning in NeuroImaging
MLNI
MLNI is a python package that performs various tasks using neuroimaging data: i) binary classification for disease diagnosis, following good practice proposed in AD-ML; ii) regression prediction, such as age prediction; and iii) semi-supervised clustering with HYDRA.
:warning: The documentation of this software is currently under development
Citing this work
If you use this software for clustering:
Varol, E., Sotiras, A., Davatzikos, C., 2017. HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage, 145, pp.346-364. doi:10.1016/j.neuroimage.2016.02.041 - Paper in PDF
If you use this software for classification or regression:
Wen, J., Samper-González, J., Bottani, S., Routier, A., Burgos, N., Jacquemont, T., Fontanella, S., Durrleman, S., Epelbaum, S., Bertrand, A. and Colliot, O., 2020. Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimer’s disease. Neuroinformatics, pp.1-22. doi:10.1007/s12021-020-09469-5 - Paper in PDF
J. Samper-Gonzalez, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon, M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou and O. Colliot, Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. NeuroImage, 183:504–521, 2018 doi:10.1016/j.neuroimage.2018.08.042 - Paper in PDF - Supplementary material
Publication using MLNI
Wen, J., Varol, E., Davatzikos, C., 2020. Multi-scale feature reduction and semi-supervised learning for parsing neuroanatomical heterogeneity. Organization for Human Brain Mapping. - Link
Wen, J., Varol, E., Davatzikos, C., 2021. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. MedIA. - Link
Wen, J., Fu, C.H., Tosun, Davatzikos, C. 2022. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression. JAMA Psychiatry - Link
Lalousis, P.A., Schmaal, L., Wood, S.J., Reniers, R.L., Barnes, N.M., Chisholm, K., Griffiths, S.L., Stainton, A., Wen, J., Hwang, G. and Davatzikos, C., 2022. Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biological Psychiatry. - Link
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