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
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