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1800 Total Publications

Man versus machine: comparison of radiologists’ interpretations and NeuroQuant(R) volumetric analyses of brain MRIs in patients with traumatic brain injury
Ross, D. E., Ochs, a L., Seabaugh, J. M., Shrader, C. R., & Alzheimer’s Disease Neuroimaging, I.
2013; Journal Journal of Neuropsychiatry and Clinical Neuroscience; vol. 25; no. 1; pp. 32-39; AWUVIqi8dzjauPDbjhrD doi:10.1176/appi.neuropsych.11120377
Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization.
Ortiz, A., G??rriz, J. M., Ram??rez, J., & Salas-Gonz??lez, D.
2013; Journal Neurocomputing; vol. 114; pp. 118-131; AWUVIqi8dzjauPDbjhrI doi:10.1016/j.neucom.2012.08.047
Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.
Eskildsen, S. F., Coupé, P., García-Lorenzo, D., Fonov, V., Pruessner, J. C., & Collins, D. L.
2013; Journal Neuroimage; vol. 65; pp. 511-521; AWUVIqi8dzjauPDbjhrK doi:10.1016/j.neuroimage.2012.09.058
Bi-level multi-source learning for heterogeneous block-wise missing data.
Xiang, S., Yuan, L., Fan, W., Wang, Y., Thompson, P. M., & Ye, J.
2013; Journal Neuroimage; vol. 102; pp. 192-206; AWUVIqi8dzjauPDbjhrL doi:10.1016/j.neuroimage.2013.08.015
Machine learning-based method for personalized and cost-effective detection of Alzheimer’s disease.
Escudero, J., Ifeachor, E., Zajicek, J. P., Green, C., Shearer, J., & Pearson, S.
2013; Journal IEEE Transactions on Bio-Medical Engineering; vol. 60; no. 1; pp. 164-8; AWUU_d3HdzjauPDbjhqi doi:10.1109/TBME.2012.2212278
Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer’s disease. Int J Data Min Bioinform
Ayhan MS, Benton RG, Raghavan V V, Choubey S.
2013; Journal International Journal of Data Mining and Bioinformatics; vol. 7; no. 2; pp. 146-65; AWUU_d3HdzjauPDbjhqk
Spatial and anatomical regularization of SVM: A general framework for neuroimaging data.
Cuingnet, R., Glaunés, J. A., Chupin, M., Benali, H., & Colliot, O.
2013; Journal IEEE Transactions on Pattern Analysis and Machine Intelligence; vol. 35; no. 3; pp. 682-696; AWUVFyWqwAl_G9zjEvgo doi:10.1109/TPAMI.2012.142
The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer’s disease.
Wu, X., Li, J., Ayutyanont, N., Protas, H., Jagust, W., Fleisher, A., … Chen, K.
2013; Journal IEEE/ACM Transactions on Computational Biology and Bioinformatics; vol. 10; no. 1; pp. 173-80; AWUVFyWqwAl_G9zjEvgp doi:10.1109/TCBB.2012.141
Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18F-FDG PET.
Shokouhi, S., Claassen, D., Kang, H., Ding, Z., Rogers, B., Mishra, A., & Riddle, W. R.
2013; Journal Journal of Nuclear Medicine; vol. 54; no. 9; pp. 1564-9; AWUVFyWqwAl_G9zjEvgw doi:10.2967/jnumed.112.116137
Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures.
Yan, J., Huang, H., Risacher, S. L., Kim, S., Inlow, M., Moore, J. H., … Shen, L.
2013; Journal Multimodal Brain Image Analysis; vol. 8159; pp. 202-210; AWUVAA3tMOwVf_qbJr72 doi:10.1007/978-3-319-02126-3_20