Spatially regularized SVM for the detection of brain areas associated with stroke outcome

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

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 316-323

  Cuingnet, Rémi ^a,b   Rosso, Charlotte ^a,c   Lehéricy, Stéphane ^a,d   Dormont, Didier ^a,d   Benali, Habib ^b   Samson, Yves ^a,c   Colliot, Olivier ^a  

^a SORBONNE UNIVERSITÉ   (France)

^b INSERM   (France)

^c ASSISTANCE PUBLIQUE HÔPITAUX DE PARIS   (France)

^d CENIR   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN AREAS; BRAIN IMAGES; CORTICOSPINAL; DIFFUSION WEIGHTED IMAGES; GRAPH ENCODING; GROUP DIFFERENCES; PREMOTOR CORTEX; STANDARD MASS; STROKE PATIENTS; UNIVARIATE ANALYSIS; WHITE MATTER TRACTS;

BRAIN; MEDICAL COMPUTING; MEDICAL IMAGING; POPULATION STATISTICS; STATISTICAL TESTS;

SUPPORT VECTOR MACHINES;

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; MIDDLE AGED; OUTCOME ASSESSMENT; PATHOLOGY; PROGNOSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STROKE;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; CLUSTER ANALYSIS; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MIDDLE AGED; OUTCOME ASSESSMENT (HEALTH CARE); PATTERN RECOGNITION, AUTOMATED; PROGNOSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STROKE;

EID: 84862172399     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_39     Document Type: Conference Paper

References (22)

1. Chalela, J., et al.: Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. The Lancet 369(9558), 293-298 (2007) (Pubitemid 46136489)
2. Crafton, K., et al.: Improved understanding of cortical injury by incorporating measures of functional anatomy. Brain 126(7), 1650-1659 (2003) (Pubitemid 36834941)
3. Domi, T., et al.: Corticospinal tract pre-wallerian degeneration: A novel outcome predictor for pediatric stroke on acute MRI. Stroke 40(3), 780-787 (2009)
4. Lo, R., et al.: Identification of critical areas for motor function recovery in chronic stroke subjects using voxel-based lesion symptom mapping. NeuroImage 49(1), 9-18 (2010)
5. Rosso, C., et al.: Prediction of Infarct Growth Based on Apparent Diffusion Coefficients: Penumbral Assessment without Intravenous Contrast Material. Radiology 250(1), 184-192 (2009)
6. Ashburner, J., Friston, K.J.: Voxel-based morphometry-the methods. NeuroImage 11(6), 805-821 (2000)
7. Davatzikos, C.: Why voxel-based morphometric analysis should be used with great caution when characterizing group differences. NeuroImage 23(1), 17-20 (2004)
8. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
9. Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2001)
10. Lao, Z., et al.: Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 21(1), 46-57 (2004) (Pubitemid 38114995)
11. Fan, Y., et al.: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE TMI 26(1), 93-105 (2007)
12. Klöppel, S., et al.: Automatic classification of MR scans in Alzheimer's disease. Brain 131(3), 681-689 (2008)
13. Vemuri, P., et al.: Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage 39(3), 1186-1197 (2008)
14. Mourão-Miranda, J., et al.: Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional MRI data. NeuroImage 28(4), 980-995 (2005); Special Section: Social Cognitive Neuroscience
15. Chung, F.R.K.: Spectral Graph Theory, vol. 92. AMS (1992)
16. Golub, G.H., Van Loan, C.F.: Matrix computations. J. Hopkins Univ. Press, Baltimore (1996)
17. Smola, A.J., Schölkopf, B.: On a kernel-based method for pattern recognition, regression, approximation, and operator inversion. Algorithmica 22, 211-231 (1998)
18. Kondor, R.I., Lafferty, J.D.: Diffusion kernels on graphs and other discrete input spaces. In: Proc. International Conference on Machine Learning, pp. 315-322 (2002)
19. Gómez-Chova, L., et al.: Semi-supervised image classification with Laplacian support vector machines. IEEE Geo. Rem. Sens. Let. 5(3), 336-340 (2008)
20. Smola, A., Kondor, R.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, p. 144. Springer, Heidelberg (2003)
21. Rapaport, F., et al.: Classification of microarray data using gene networks. BMC bioinformatics 8(1), 35 (2007)
22. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B. Met., 289-300 (1995)