| scholarly article | Q13442814 |
| P50 | author | Christos Davatzikos | Q29348045 |
| Ying Wang | Q57000244 | ||
| Yong Fan | Q112829775 | ||
| P2093 | author name string | Priyanka Bhatt | |
| P2860 | cites work | The Mini-Mental State Examination: A Comprehensive Review | Q22242356 |
| The use of multiple measurements in taxonomic problems | Q22667799 | ||
| The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods | Q24647259 | ||
| Voxel-based morphometry–the methods | Q28138780 | ||
| A fast diffeomorphic image registration algorithm | Q28243996 | ||
| Bagging predictors | Q29013802 | ||
| Watersheds in digital spaces: an efficient algorithm based on immersion simulations | Q29013909 | ||
| Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies | Q30481978 | ||
| Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data | Q31019950 | ||
| A nonparametric method for automatic correction of intensity nonuniformity in MRI data | Q32061718 | ||
| Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. | Q34244667 | ||
| Predictors of conversion to dementia of probable Alzheimer type in patients with mild cognitive impairment | Q36447010 | ||
| Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging | Q36571382 | ||
| Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. | Q36861179 | ||
| Automatic classification of MR scans in Alzheimer's disease | Q36965246 | ||
| Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index | Q37270612 | ||
| MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations | Q37273551 | ||
| MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change | Q37273569 | ||
| Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning | Q43997157 | ||
| Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. | Q44870170 | ||
| A general and unifying framework for feature construction, in image-based pattern classification. | Q45963524 | ||
| COMPARE: classification of morphological patterns using adaptive regional elements. | Q45965571 | ||
| Morphological classification of brains via high-dimensional shape transformations and machine learning methods. | Q45966871 | ||
| A global geometric framework for nonlinear dimensionality reduction | Q47014659 | ||
| Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex | Q47820499 | ||
| Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy | Q48712824 | ||
| Improved watershed transform for medical image segmentation using prior information. | Q51999528 | ||
| Analysis of a complex of statistical variables into principal components | Q56445548 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Predicting clinical variable from MRI features: application to MMSE in MCI | Q57042035 | ||
| A tutorial on support vector regression | Q62065973 | ||
| P4510 | describes a project that uses | machine learning | Q2539 |
| P433 | issue | 4 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | medical imaging | Q931309 |
| machine learning | Q2539 | ||
| P304 | page(s) | 1519-35 | |
| P577 | publication date | 2010-05-01 | |
| P1433 | published in | NeuroImage | Q1981225 |
| P1476 | title | High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables | |
| P478 | volume | 50 |
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| Q36758255 | The relevance voxel machine (RVoxM): a self-tuning Bayesian model for informative image-based prediction |
| Q94566937 | Towards a brain-based predictome of mental illness |
| Q38906604 | Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry |
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