The DOI matches its Crossref record; 90 of the 92 references it cites were found in a registry or an index.
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
Open accessAbstract
For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 1×
“Those were distributed around a medial temporal lobe (that includes amygdala, hippocampal formation, entorhinal cortex) (Braak and Braak, 1991; Visser et al., 2002; Mosconi, 2005; Lee et al., 2006; Devanand et al., 2007; Burton et al., 2009; Desikan et al., 2009; Ewers et al., 2012; Walhovd et al., 2010), superior/med…”
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
(2011) · Cited by 2,752
Functional and effective connectivity in neuroimaging: a synthesis
Friston, K. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020107 · Cited by 2,452
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890
Acoustic Modeling Using Deep Belief Networks
Mohamed, A., Dahl, G. E., & Hinton, G. E. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,756
Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056181 · Cited by 1,578
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks
Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,340
Self-citationHAMMER: hierarchical attribute matching mechanism for elastic registration
Shen, D. G., & Davatzikos, C. A. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.803111 · Cited by 1,089
Self-citationMultimodal Learning with Deep Boltzmann Machines
Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070
2012 Alzheimer's disease facts and figures Alzheimer's Association ∗
Thies, W. H., & Bleiler, L. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.02.001 · Cited by 1,014
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Olivier Colliot, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 995
Brain glucose metabolism in the early and specific diagnosis of Alzheimer?s disease
Mosconi, L. (2005) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-005-1762-7 · Cited by 938
Hippocampal and entorhinal atrophy in mild cognitive impairment
Devanand, D. P., Pradhaban, G., Liu, X., Khandji, A., Santi, S. D., Segal, S., Rusinek, H., Pelton, G. H., Honig, L. S., Mayeux, R. P., et al. (2007) · Neurology · doi:10.1212/01.wnl.0000256697.20968.d7 · Cited by 749
Multimodal techniques for diagnosis and prognosis of Alzheimer's disease
Perrin, R. J., Fagan, A. M., & Holtzman, D. M. (2009) · Nature · doi:10.1038/nature08538 · Cited by 702
In Vivo Mapping of Gray Matter Loss with Voxel-Based Morphometry in Mild Alzheimer's Disease
Baron, J., Chételat, G., Desgranges, B., Perchey, G., Landeau, B., Sayette, V. D. L., & Eustache, F. (2001) · NeuroImage · doi:10.1006/nimg.2001.0848 · Cited by 699
Zhang, D., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.069 · Cited by 689
Self-citationClassification using discriminative restricted Boltzmann machines
Larochelle, H., & Bengio, Y. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390224 · Cited by 666
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Davatzikos, C. A., Bhatt, P., Shaw, L. M. J., Batmanghelich, K. N., & Trojanowski, J. Q. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.023 · Cited by 596
Johnson, N. A., Jahng, G., Weiner, M. W., Miller, B. L., Chui, H. C., Jagust, W. J., Gorno‐Tempini, M. L., & Schuff, N. W. (2005) · Radiology · doi:10.1148/radiol.2343040197 · Cited by 596
Mild cognitive impairment: Disparity of incidence and prevalence estimates
Ward, A., Arrighi, H. M., Michels, S., & Cedarbaum, J. M. (2012) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.01.002 · Cited by 569
Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study
de Jong, L. W., van der Hiele, K., Veer, I. M., Houwing, J. J., Westendorp, R. G. J., Bollen, E. L. E. M., de Bruin, P. W., Middelkoop, H. A. M., van Buchem, M. A., & van der Grond, J. (2008) · Brain · doi:10.1093/brain/awn278 · Cited by 557
Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488
An Efficient Learning Procedure for Deep Boltzmann Machines
Salakhutdinov, R., & Hinton, G. E. (2012) · Neural Computation · doi:10.1162/neco_a_00311 · Cited by 477
The use of PET in Alzheimer disease
Nordberg, A. K., Rinne, J. O., Kadir, A., & Långström, B. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.217 · Cited by 467
Deep Learning-Based Feature Representation for AD/MCI Classification
Suk, H., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_72 · Cited by 463
Self-citationEwers, M., Walsh, C., Trojanowski, J. Q., Shaw, L. M. J., Petersen, R. C., Jack, C. R., Feldman, H., Bokde, A. L. W., Alexander, G. E., Scheltens, P., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.10.019 · Cited by 421
Training-related brain plasticity in subjects at risk of developing Alzheimer’s disease
Belleville, S., Clément, F., Mellah, S., Gilbert, B., Fontaine, F. S., & Gauthier, S. G. (2011) · Brain · doi:10.1093/brain/awr037 · Cited by 420
PET/MRI: Paving the Way for the Next Generation of Clinical Multimodality Imaging Applications
Pichler, B. J., Kolb, A., Nägele, T., & Schlemmer, H.-P. (2010) · Journal of Nuclear Medicine · doi:10.2967/jnumed.109.061853 · Cited by 418
Chris Hinrichs, A., Singh, V., Xu, G., & Johnson, S. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.081 · Cited by 416
Davatzikos, C. A., Genç, A., Xu, D., & Resnick, S. M. (2001) · NeuroImage · doi:10.1006/nimg.2001.0937 · Cited by 416
Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease
Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Greve, D. N., Salat, D. H., Buckner, R. L., et al. (2009) · Brain · doi:10.1093/brain/awp123 · Cited by 405
Dai, W., López, O. L., Carmichael, O. T., Becker, J. T., Kuller, L. H., & Gach, H. M. (2009) · Radiology · doi:10.1148/radiol.2503080751 · Cited by 393
Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease
Singh, V., Chertkow, H., Lerch, J. P., Evans, A. C., Dorr, A. E., & Kabani, N. J. (2006) · Brain · doi:10.1093/brain/awl256 · Cited by 386
Burton, E. J., Barber, R., Mukaetova‐Ladinska, E. B., Robson, J., Perry, R. H., Jaros, E., Kalaria, R. N., & O'Brien, J. T. (2008) · Brain · doi:10.1093/brain/awn298 · Cited by 385
Identification of MCI individuals using structural and functional connectivity networks
Chong-yaw Wee, A., Yap, P., Zhang, D., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.015 · Cited by 378
Self-citationCOMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements
Yong Fan, A., Shen, D. G., Gur, R. C., Gur, R. E., & Davatzikos, C. A. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.886812 · Cited by 370
Self-citationVisser, P. J., Verhey, F. R. J., Hofman, P. A. M., Scheltens, P., & Jolles, J. (2002) · PubMed Central · doi:10.1136/jnnp.72.4.491 · Cited by 359
Ensemble sparse classification of Alzheimer's disease
Liu, M., Zhang, D., & Shen, D. G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.055 · Cited by 326
Self-citationWestman, E., Muehlboeck, J., & Simmons, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.04.056 · Cited by 310
P1‐071: Intensity of dementia through latent variable modelling (I‐DeLV) in the AIBL cohort
Burnham, S. C., Graham, P. L., Wilson, B., Ames, D. J., Macaulay, L. S., Martins, R. N., Masters, C. L., Maruff, P. T., Rowe, C. C., Szoeke, C. E. I., et al. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.05.347 · Cited by 292
PET/MRI for Neurologic Applications
Catana, C., Drzezga, A. E., Heiss, W., & Rosen, B. R. (2012) · Journal of Nuclear Medicine · doi:10.2967/jnumed.112.105346 · Cited by 292
Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
Zhang, D., Shen, D. G., & Initiative, A. D. N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033182 · Cited by 289
Self-citationWalhovd, K. B., Fjell, A. M., Brewer, J., McEvoy, L. K., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Karow, D., Dale, A. M., & Initiative, T. A. D. N. (2010) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1809 · Cited by 279
Bokde, A. L. W., Lopez‐Bayo, P., Meindl, T., Pechler, S., Born, C., Faltraco, F., Teipel, S. J., Möller, H.-J., & Hampel, H. J. (2006) · Brain · doi:10.1093/brain/awl051 · Cited by 261
Cui, Y. J., Liu, B., Luo, S., Zhen, X., Fan, M., Liu, T., Zhu, W., Park, M., Jiang, T., Jin, J. S., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021896 · Cited by 238
Li, Y., Wang, Y., Guorong Wu, A., Shi, F., Zhou, L., Lin, W., & Shen, D. G. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.11.008 · Cited by 203
Self-citationEnriched white matter connectivity networks for accurate identification of MCI patients
Chong-yaw Wee, A., Yap, P., Li, W., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L. V., & Shen, D. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.026 · Cited by 200
Self-citationYuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.059 · Cited by 195
Ishii, K., Kawachi, T., Sasaki, H., Kono, A. K., Fukuda, T., KOJIMA, Y., & Mori, E. (2005) · PubMed Central · Cited by 192
Boosting power for clinical trials using classifiers based on multiple biomarkers
Kohannim, O., Hua, X., Hibar, D. P., Lee, S., Chou, Y.-Y., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.022 · Cited by 190 · cited 10×
“As for the fusion ofmultiplemodalities includingMRI, PET, biological and neurological data for discriminating AD/MCI patients from healthy Normal Control (NC), Kohannim et al. (2010) concatenated features from modalities into a vector and used a Support Vector Machine (SVM) classifier.”
InfluentialSelf-citation3D Anatomical Atlas of the Human Brain
Kabani, N. J., MacDonald, D. J., Holmes, C. J., & Evans, A. C. (1998) · NeuroImage · doi:10.1016/s1053-8119(18)31550-7 · Cited by 167
Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation
Liao, S., Gao, Y., Oto, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_32 · Cited by 157
Self-citationRestricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks
Hjelm, R. D., Calhoun, V. D., Salakhutdinov, R., Allen, E. A., Adalı, T., & Plis, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.048 · Cited by 154
Lee, A. C. H., Buckley, M. J., Gaffan, D., Emery, T., Hodges, J. R., & Graham, K. S. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.3157-05.2006 · Cited by 147
Multivariate examination of brain abnormality using both structural and functional MRI
Yong Fan, A., Rao, H., Hurt, H., Giannetta, J. M., Korczykowski, M., Shera, D. M., Avants, B., Gee, J. C., Wang, J., & Shen, D. G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.009 · Cited by 135
Self-citationHierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis
Liu, M., Zhang, D., Shen, D. G., & Initiative, T. A. D. N. (2013) · Human Brain Mapping · doi:10.1002/hbm.22254 · Cited by 133
Self-citationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2012
Ayache, N., Delingette, H., Golland, P., & Mori, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2 · Cited by 128
ABSORB: Atlas building by self-organized registration and bundling
Jia, H., Guorong Wu, A., Wang, Q., & Shen, D. G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.010 · Cited by 112
Self-citationWang, Y., Nie, J., Yap, P., Li, G., Shi, F., Geng, X., Guo, L., Shen, D. G., & Initiative, A. D. N. (2014) · PLoS ONE · doi:10.1371/journal.pone.0077810 · Cited by 93
Self-citationHierarchical Anatomical Brain Networks for MCI Prediction: Revisiting Volumetric Measures
Zhou, L., Wang, Y., Yang, L., Yap, P., Shen, D. G., & (ADNI), A. T. A. D. N. I. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021935 · Cited by 85
Self-citationXUE, Z., Shen, D. G., & Davatzikos, C. A. (2006) · Medical Image Analysis · doi:10.1016/j.media.2006.06.007 · Cited by 83
Self-citationRABBIT: Rapid alignment of brains by building intermediate templates
Songyuan, T., Yong Fan, A., Guorong Wu, A., Kim, M., & Shen, D. G. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.043 · Cited by 82
Self-citationMulti-scale classification of disease using structural MRI and wavelet transform
Ritter, K., Paul, F., Weygandt, M., Allefeld, C., & Haynes, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.05.022 · Cited by 78
Diffusion Tensor Image Registration Using Tensor Geometry and Orientation Features
Yang, J., Shen, D. G., Davatzikos, C. A., & Verma, R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_109 · Cited by 76
Self-citationImplicit Mixtures of Restricted Boltzmann Machines
Nair, V. B., & Hinton, G. E. (2008) · Cited by 72
Advances in Neural Information Processing Systems 10 (NIPS 1997)
Goldberg, P. W., Williams, C. K. I., & Bishop, C. (1997) · Cited by 70
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009
García-Lorenzo, D., Lecoeur, J., Arnold, D. L., Collins, D. L., & Barillot, C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3 · Cited by 62
An investigation of PreMCI: Subtypes and longitudinal outcomes
Loewenstein, D., Greig, M. T., Schinka, J. A., Barker, W., Shen, Q., Potter, E. L., Raj, A., Brooks, L., Varón, D., Schoenberg, M. R., et al. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.03.002 · Cited by 55
Discriminative Group Sparse Representation for Mild Cognitive Impairment Classification
Suk, H., Chong-yaw Wee, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02267-3_17 · Cited by 38
Self-citationMark, R. E., & Sitskoorn, M. M. (2012) · Reviews in Clinical Gerontology · doi:10.1017/s0959259812000172 · Cited by 33
A Theory of Mean Field Approximation
Tanaka, T. (1998) · Cited by 29
Machine Learning in Medical Imaging
Suzuki, K., Yan, P., Wang, F., & Shen, D. G. (2012) · International Journal of Biomedical Imaging · doi:10.1155/2012/123727 · Cited by 27
Self-citationA Wavelet-Based Statistical Analysis of fMRI Data: I. Motivation and Data Distribution Modeling
Dinov, I. D., Boscardin, W. J., Mega, M. S., Sowell, E. R., & Toga, A. W. (2005) · Neuroinformatics · doi:10.1385/ni:3:4:319 · Cited by 27
Deep Boltzmann Machines as Feed-Forward Hierarchies
Montavon, G., Braun, M. L., & Müller, K. R. (2012) · Cited by 14
Flagged first, then by citations
2 of the 92 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Self-citationMultimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
Razzak, I., Naz, S., & Zaib, A. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,363
Metadata
- Authors
- Suk, H.-I., Lee, S.-W., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Korea University (KR)
- Corresponding author
- Shen, Dinggang
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 101
- Pages
- 569-582
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4165842
- OpenAlex
- W2126598020
- Cited by
- 968
- References
- 92
- Citations by year
- 2026: 62 · 2025: 74 · 2024: 81 · 2023: 93 · 2022: 104 · 2021: 128 · 2020: 117 · 2019: 107 · 2018: 85 · 2017: 57 · 2016: 41 · 2015: 17 · 2014: 1
- FWCI
- 29.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- NIH (EB006733, EB008374, EB009634, AG041721, MH100217, AG042599)
- National Research Foundation (2012-005741)
- Korean Government
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- Alzheimer's disease, mild cognitive impairment, multimodal fusion, MRI-PET fusion, hierarchical feature representation, deep Boltzmann machine, neuroimaging diagnosis, MCI conversion, ADNI
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Artificial Intelligence, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neural Networks, Computer, Positron-Emission Tomography, Cognitive Dysfunction, Multimodal Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
