MKL for robust multi-modality AD classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5762 LNCS, Issue PART 2, 2009, Pages 786-794

  Hinrichs, Chris ^a,b   Singh, Vikas ^a,b   Xu, Guofan ^b   Johnson, Sterling ^b  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALZHEIMER'S DISEASE; APRIORI; DATA SETS; HEALTHY INDIVIDUALS; IMAGE DATA; IMAGING MODALITY; KERNEL WEIGHT; MACHINE LEARNING METHODS; MAXIMUM MARGIN; MULTI-KERNEL; MULTI-MODAL; MULTI-MODAL IMAGE; MULTI-MODALITY; MULTIPLE MODALITIES; PET IMAGES;

ADAPTIVE BOOSTING; CLASSIFIERS; LEARNING SYSTEMS; MEDICAL COMPUTING; OPTIMIZATION;

CLASSIFICATION (OF INFORMATION);

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; POSITRON-EMISSION TOMOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 80054059398     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_95     Document Type: Conference Paper

References (12)

1. Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., et al.: The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging (2008) (Pubitemid 351508080)
2. Apostolova, L.G., Thompson, P.M.: Brain mapping as a tool to study neurodegeneration. Neurotherapeutics 4(3), 387-400 (2007)
3. Ashburner, J., Friston, K.J.: Voxel-Based Morphometry - the methods. Neuroimage 11(6), 805-821 (2000)
4. Fan, Y., Resnick, S.M., Wu, X., Davatzikos, C.: Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. Neuroimage 41(2), 277-285 (2008)
5. Klöppel, S., Stonnington, C.M., Chu, C., Draganski, B., et al.: Automatic classification of MR scans in Alzheimer's disease. Brain 131(3), 681-689 (2008) (Pubitemid 351294712)
6. Vemuri, P., Gunter, J.L., Senjem, M.L., Whitwell, J.L., et al.: Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39(3), 1186-1197 (2008)
7. Hinrichs, C., Singh, V., Mukherjee, L., Chung, M.K., Xu, G., Johnson, S.C.: Spatially Augmented LPBoosting with evaluations on the ADNI dataset. Neuroimage (in press, 2009)
8. Schölkopf, B., Smola, A.: Learning with kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
9. Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. Journ. of Machine Learning Research 7, 1531-1565 (2006) (Pubitemid 44373694)
10. Xu, L., Crammer, K., Schuurmans, D.: Robust support vector machine training via convex outlier ablation. In: Proc. of AAAI (2006) (Pubitemid 44705339)
11. Jack, C., Petersen, R., Xu, Y., O'Brien, P., et al.: Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55(4), 484-490 (2000) (Pubitemid 30647242)
12. Minoshima, S., Giordani, B., Berent, S., Frey, K.A.: et al.: Metabolic reduction in the posterior cingulate cortex in very early alzheimer's disease. Ann. Neurol., 85-94 (1997)