Multiple instance learning for classification of dementia in brain MRI

Medical Image Analysis

Volumn 18, Issue 5, 2014, Pages 808-818

  Tong, Tong ^a   Wolz, Robin ^a   Gao, Qinquan ^a   Guerrero, Ricardo ^a   Hajnal, Joseph V ^b   Rueckert, Daniel ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b ST THOMAS HOSPITAL   (United Kingdom)

Author keywords

Alzheimer's disease; Classification; Multiple instance learning; Structural MR imaging

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES;

ALZHEIMER'S DISEASE; LEAVE-ONE-OUT CROSS VALIDATIONS; MACHINE LEARNING TECHNIQUES; MILD COGNITIVE IMPAIRMENTS (MCI); MR IMAGING; MULTIPLE INSTANCE LEARNING; STATE-OF-THE-ART METHODS; WEAKLY SUPERVISED LEARNING;

DIAGNOSIS;

ALZHEIMER DISEASE; ARTICLE; DEMENTIA; DISEASE CLASSIFICATION; HIPPOCAMPUS; HUMAN; IMAGE PROCESSING; LEARNING DISORDER; MACHINE LEARNING; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; NEUROLOGIC EXAMINATION; NUCLEAR MAGNETIC RESONANCE IMAGING; PARAMETERS; PRIORITY JOURNAL; AGED; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; FEMALE; IMAGE ENHANCEMENT; MALE; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGED; ALGORITHMS; ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BRAIN; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84901016404     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.04.006     Document Type: Article

References (63)

1. Amari S.-i., Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks 1999, 12(6):783-789.
2. Andrews S., Tsochantaridis I., Hofmann T. Support vector machines for multiple-instance learning. Adv. Neural Inf. Process. Syst. 2002, 15:561-568.
3. Babenko, B., Yang, M.-H., Belongie, S., 2009. Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990.
4. Bi, J., Liang, J., 2007. Multiple instance learning of pulmonary embolism detection with geodesic distance along vascular structure. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
5. Brookmeyer R., Johnson E., Ziegler-Graham K., Arrighi H.M. Forecasting the global burden of Alzheimers disease. Alzheimer's Dementia 2007, 3(3):186-191.
6. Cawley G.C., Talbot N.L. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks 2004, 17(10):1467-1476.
7. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2:27:1-27:27. software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.
8. Cho Y., Seong J.-K., Jeong Y., Shin S.Y. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 2012, 59(3):2217-2230.
9. Chu C., Hsu A.-L., Chou K.-H., Bandettini P., Lin C.-P. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images. NeuroImage 2011, 60:59-70.
10. Clark C., Davatzikos C., Borthakur A., Newberg A., Leight S., Lee V.-Y., Trojanowski J. Biomarkers for early detection of Alzheimer pathology. Neurosignals 2007, 16(1):11-18.
11. Coupé P., Eskildsen S.F., Manjón J.V., Fonov V.S., Pruessner J.C., Allard M., Collins D.L. Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease. NeuroImage: Clinical 2012, 1(1):141-152.
12. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehéricy S., Habert M.-O., Chupin M., Benali H., Colliot O. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 2011, 56(2):766-781.
13. Eskildsen S.F., Coupé P., García-Lorenzo D., Fonov V., Pruessner J.C., Collins D.L. Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. NeuroImage 2013, 65:511-521.
14. Fan Y., Shen D., Gur R.C., Gur R.E., Davatzikos C. COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 2007, 26(1):93-105.
15. Fu Z., Robles-Kelly A., Zhou J. MILIS: multiple instance learning with instance selection. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(5):958-977.
16. Gerardin E., Chételat G., Chupin M., Cuingnet R., Desgranges B., Kim H.-S., Niethammer M., Dubois B., Lehéricy S., Garnero L., et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. Neuroimage 2009, 47(4):1476.
17. Ghosh D., Chinnaiyan A.M. Classification and selection of biomarkers in genomic data using LASSO. BioMed Res. Int. 2005, 2005(2):147-154.
18. Gray K.R., Wolz R., Heckemann R.A., Aljabar P., Hammers A., Rueckert D. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease. NeuroImage 2012, 60(1):221-229.
19. Gray K.R., Aljabar P., Heckemann R.A., Hammers A., Rueckert D. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. Neuroimage 2013, 65:167-175.
20. Grundman M., Petersen R.C., Ferris S.H., Thomas R.G., Aisen P.S., Bennett D.A., Foster N.L., Jack C.R., Galasko D.R., Doody R., et al. Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch. Neurol. 2004, 61(1):59.
21. Herholz K., Salmon E., Perani D., Baron J., Holthoff V., Frölich L., Schönknecht P., Ito K., Mielke R., Kalbe E., et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG-PET. Neuroimage 2002, 17(1):302-316.
22. Hinrichs C., Singh V., Mukherjee L., Xu G., Chung M.K., Johnson S.C. Spatially augmented lpboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 2009, 48(1):138-149.
23. Hinrichs C., Singh V., Xu G., Johnson S.C. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 2011, 55(2):574-589.
24. Huan J., Bandyopadhyay D., Wang W., Snoeyink J., Prins J., Tropsha A. Comparing graph representations of protein structure for mining family-specific residue-based packing motifs. J. Comput. Biol. 2005, 12(6):657-671.
25. Jack C., Bernstein M., et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 2008, 27(4):685-691.
26. Jack Jr C.R., Knopman D.S., Jagust W.J., Petersen R.C., Weiner M.W., Aisen P.S., Shaw L.M., Vemuri P., Wiste H.J., Weigand S.D., et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013, 12(2):207-216.
27. Janousova E., Vounou M., Wolz R., Gray K., Rueckert D., Montana G. Biomarker discovery for sparse classification of brain images in Alzheimer's disease. Ann. Brit. Mach. Vision Assoc.(BMVA) 2012, 2012(2):1-11.
28. Keihaninejad S., Zhang H., Ryan N.S., Malone I.B., Modat M., Cardoso M.J., Cash D., Fox N.C., Ourselin S. An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. NeuroImage 2013, 72:153-163.
29. Koikkalainen J., Lötjönen J., Thurfjell L., Rueckert D., Waldemar G., Soininen H. Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease. NeuroImage 2011, 56(3):1134-1144.
30. Kriegeskorte N., Simmons W.K., Bellgowan P.S., Baker C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 2009, 12(5):535-540.
31. Lerch J.P., Evans A.C. Cortical thickness analysis examined through power analysis and a population simulation. Neuroimage 2005, 24(1):163-173.
32. Leung K.K., Barnes J., Modat M., Ridgway G.R., Bartlett J.W., Fox N.C., Ourselin S. Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. Neuroimage 2011, 55(3):1091-1108.
33. Liu M., Zhang D., Shen D. Ensemble sparse classification of Alzheimer's disease. Neuroimage 2012, 60(2):1106-1116.
34. Liu, M., Zhang, D., Yap, P.-T., Shen, D., 2012b. Tree-Guided Sparse Coding for Brain Disease Classification, MICCAI, pp. 239-247.
35. Liu, M., Zhang, D., Shen, D., 2013. Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Human Brain Mapping doi: 10.1002/hbm.22254.
36. Lötjönen J., Wolz R., Koikkalainen J., Julkunen V., Thurfjell L., Lundqvist R., Waldemar G., Soininen H., Rueckert D. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease. Neuroimage 2011, 56(1):185-196.
37. Lu, L., Bi, J., Wolf, M., Salganicoff, M., 2011. Effective 3D object detection and regression using probabilistic segmentation features in CT images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1049-1056.
38. Maron O., Lozano-Pérez T. A framework for multiple-instance learning. Adv. Neural Inform. Process. Syst. 1998, 570-576.
39. Mika S., Ratsch G., Weston J., Scholkopf B., Mullers K. Fisher discriminant analysis with kernels. IEEE Workshop on Neural Networks for Signal Processing IX 1999, 41-48. IEEE.
40. Neuhaus M., Bunke H. A quadratic programming approach to the graph edit distance problem. IAPR Workshop on Graph-Based Representations in Pattern Recognition 2007, 92-102.
41. Nyúl L.G., Udupa J.K. On standardizing the MR image intensity scale. Magn. Reson. Med. 1999, 42(6):1072.
42. Pihlajamäki M., Sperling R.A. fMRI: use in early Alzheimers disease and in clinical trials. Future Neurol. 2008, 3(4):409-421.
43. Pless R., Souvenir R. A survey of manifold learning for images. IPSJ Trans. Comput. Vision Applic. 2009, 1(0):83-94.
44. Proverb C. A probabilistic atlas of the human brain: theory and rationale for its development. Neuroimage 1995, 2:89-101.
45. Ranginwala N.A., Hynan L.S., Weiner M.F., White C.L. Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. Am. J. Geriatric Psych 2008, 16(5):384-388.
46. Rueckert D., Sonoda L.I., Hayes C., Hill D.L.G., Leach M.O., Hawkes D.J. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 1999, 18(8):712-721.
47. Sanchez A., David V. Advanced support vector machines and kernel methods. Neurocomputing 2003, 55(1):5-20.
48. Schölkopf B., Smola A., Müller K.-R. Kernel principal component analysis. International Conference on Artificial Neural Networks 1997, 583-588. Springer.
49. Shervashidze, N., Petri, T., Mehlhorn, K., Borgwardt, K.M., Viswanathan, S., 2009. Efficient graphlet kernels for large graph comparison. In: International Conference on Artificial Intelligence and Statistics, 2009, pp. 488-495.
50. Tong, T., Wolz, R., Gao, Q., Hajnal, J.V., Rueckert, D., 2013. Multiple Instance Learning for Classification of Dementia in Brain MRI, MICCAI, pp. 599-606.
51. Vounou M., Janousova E., Wolz R., Stein J.L., Thompson P.M., Rueckert D., Montana G. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. Neuroimage 2012, 60(1):700-716.
52. Wee C.-Y., Yap P.-T., Li W., Denny K., Browndyke J.N., Potter G.G., Welsh-Bohmer K.A., Wang L., Shen D. Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 2011, 54(3):1812-1822.
53. Wee, C.-Y., Yap, P.-T., Shen, D., 2012a. Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns. Human Brain Mapping doi: 10.1002/hbm.22156.
54. Wee C.-Y., Yap P.-T., Zhang D., Denny K., Browndyke J.N., Potter G.G., Welsh-Bohmer K.A., Wang L., Shen D. Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 2012, 59(3):2045-2056.
55. Wolz R., Heckemann R.A., Aljabar P., Hajnal J.V., Hammers A., Lötjönen J., Rueckert D., et al. Measurement of hippocampal atrophy using 4D graph-cut segmentation: application to ADNI. NeuroImage 2010, 52(1):109.
56. Wolz R., Julkunen V., Koikkalainen J., Niskanen E., Zhang D.P., Rueckert D., Soininen H., Lötjönen J. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PloS one 2011, 6(10):e25446.
57. Wolz R., Aljabar P., Hajnal J.V., Lötjönen J., Rueckert D. Nonlinear dimensionality reduction combining MR imaging with non-imaging information. Medical Image Anal. 2012, 16(4):819-830.
58. Wyman B.T., Harvey D.J., Crawford K., Bernstein M.A., Carmichael O., Cole P.E., Crane P.K., DeCarli C., Fox N.C., Gunter J.L., et al. Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimer's Dementia 2013, 9(3):332-337.
59. Xu, Y., Zhang, J., Chang, E., Lai, M., Tu, Z., 2012. Context-constrained multiple instance learning for histopathology image segmentation, MICCAI, pp. 623-630.
60. Yiannopoulou K.G., Papageorgiou S.G. Current and future treatments for Alzheimers disease. Therap. Adv. Neurol. Disorders 2013, 6(1):19-33.
61. Yoon U., Lee J.-M., Im K., Shin Y.-W., Cho B.H., Kim I.Y., Kwon J.S., Kim S.I. Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. Neuroimage 2007, 34(4):1405-1415.
62. Zhang D., Wang Y., Zhou L., Yuan H., Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage 2011, 55(3):856-867.
63. Zhou Z.-H., Sun Y.-Y., Li Y.-F. Multi-instance learning by treating instances as non-IID samples. Int. Conf. Mach. Learn. 2009, 1249-1256.