K. Aderghal, J. Benois-pineau, K. Afdel, and C. Gwenaëlle, FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+? projections, 15th International Workshop on Content-Based Multimedia Indexing, p.34, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01674952

K. Aderghal, M. Boissenin, J. Benois-pineau, G. Catheline, K. Afdel et al., Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+? Study on ADNI, pp.690-701, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01436299

K. Aderghal, A. Khvostikov, A. Krylov, J. Benois-pineau, K. Afdel et al., Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning, IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), pp.345-350, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01865970

N. Amoroso, D. Diacono, A. Fanizzi, M. La-rocca, A. Monaco et al., Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge, J. Neurosci. Methods, vol.302, pp.3-9, 2018.

J. Ashburner, A fast diffeomorphic image registration algorithm, Neuroimage, vol.38, pp.95-113, 2007.

J. Ashburner and K. J. Friston, Unified segmentation, Neuroimage, vol.26, pp.839-851, 2005.

B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med. Image Anal, vol.12, pp.26-41, 2008.

K. Bäckström, M. Nazari, I. Y. Gu, and A. S. Jakola, An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images, 2018 IEEE 15th International Symposium on Biomedical Imaging, pp.149-153, 2018.

I. Bankman, Handbook of Medical Image Processing and Analysis, 2008.

S. Basaia, F. Agosta, L. Wagner, E. Canu, G. Magnani et al., Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks, Neuroimage Clin, vol.21, 2019.

D. Baskar, V. S. Jayanthi, and A. N. Jayanthi, An efficient classification approach for detection of Alzheimer's disease from biomedical imaging modalities, Multimed. Tools Appl. 1-33, 2018.

J. Bernal, K. Kushibar, D. S. Asfaw, S. Valverde, A. Oliver et al., Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review, Artif. Intell. Med, 2018.

N. Bhagwat, J. D. Viviano, A. N. Voineskos, and M. M. Chakravarty, Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data, PLoS Comput. Biol, vol.14, 2018.

H. Braak and E. Braak, Evolution of neuronal changes in the course of Alzheimer's disease, Journal of Neural Transmission. Supplementa, pp.127-140, 1998.

R. Brookmeyer, E. Johnson, K. Ziegler-graham, and H. M. Arrighi, Forecasting the global burden of Alzheimer's disease, Alzheimers. Dement, vol.3, pp.186-191, 2007.

D. Cárdenas-peña, D. Collazos-huertas, and G. Castellanos-dominguez, Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis, Front. Neurosci, vol.11, p.413, 2017.

D. Cárdenas-peña, D. Collazos-huertas, and G. Castellanos-dominguez, Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis, Comput. Math. Methods Med, p.9523849, 2016.

A. Chaddad, C. Desrosiers, and T. Niazi, Deep Radiomic Analysis of MRI Related to Alzheimer's Disease, IEEE Access, vol.6, pp.58213-58221, 2018.

D. Cheng and M. Liu, CNNs based multi-modality classification for AD diagnosis, 10th International Congress on Image and Signal Processing, pp.1-5, 2017.

D. Cheng, M. Liu, J. Fu, and Y. Wang, Classification of MR brain images by combination of multi-CNNs for AD diagnosis, Ninth International Conference on Digital Image Processing (ICDIP). Presented at the Ninth International Conference on Digital Image Processing, p.1042042, 2017.

F. Çitak-er, D. Goularas, and B. Ormeci, A novel Convolutional Neural Network Model Based on Voxel-based Morphometry of Imaging Data in Predicting the Prognosis of Patients with Mild Cognitive Impairment, J. Neurol. Sci. Turk, vol.34, 2017.

R. Cui, M. Liu, and G. Li, Longitudinal analysis for Alzheimer's disease diagnosis using RNN, IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp.1398-1401, 2018.

B. C. Dickerson, I. Goncharova, M. P. Sullivan, C. Forchetti, R. S. Wilson et al., MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer's disease?, Neurobiol. Aging, vol.22, pp.747-754, 2001.

C. V. Dolph, M. Alam, Z. Shboul, M. D. Samad, and K. M. Iftekharuddin, Deep learning of texture and structural features for multiclass Alzheimer's disease classification, International Joint Conference on Neural Networks (IJCNN), pp.2259-2266, 2017.

B. Duraisamy, J. V. Shanmugam, and J. Annamalai, Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network, Brain Imaging Behav, vol.13, pp.87-110, 2019.

K. A. Ellis, A. I. Bush, D. Darby, D. De-fazio, J. Foster et al., The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease, Int. Psychogeriatr, vol.21, pp.672-687, 2009.

K. A. Ellis, C. C. Rowe, V. L. Villemagne, R. N. Martins, C. L. Masters et al., Addressing population aging and Alzheimer's disease through the Australian Imaging Biomarkers and Lifestyle study: Collaboration with the Alzheimer's Disease Neuroimaging Initiative, 2010.

S. Esmaeilzadeh, D. I. Belivanis, K. M. Pohl, and E. Adeli, End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI, Machine Learning in Medical Imaging, pp.337-345, 2018.

M. Ewers, R. A. Sperling, W. E. Klunk, M. W. Weiner, and H. Hampel, Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia, Trends Neurosci, vol.34, pp.430-442, 2011.

F. Falahati, E. Westman, and A. Simmons, Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging, J. Alzheimers. Dis, vol.41, pp.685-708, 2014.

A. Farooq, S. Anwar, M. Awais, and S. Rehman, A deep CNN based multi-class classification of Alzheimer's disease using MRI, 2017 IEEE International Conference on Imaging Systems and Techniques (IST), pp.1-6, 2017.

V. Fonov, M. Dadar, P. The, L. Group, and D. Collins, Deep learning of quality control for stereotaxic registration of human brain MRI, 2018.

V. Fonov, A. C. Evans, K. Botteron, C. R. Almli, R. C. Mckinstry et al., Unbiased average age-appropriate atlases for pediatric studies, Brain Development Cooperative Group, vol.54, pp.313-327, 2011.

V. S. Fonov, A. C. Evans, R. C. Mckinstry, C. R. Almli, and D. L. Collins, Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, Neuroimage Supplement, vol.1, 2009.

I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, K. Gorgolewski et al., Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python, Front. Neuroinform, vol.5, 2011.

K. J. Gorgolewski, T. Auer, V. D. Calhoun, R. C. Craddock, S. Das et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Sci Data, vol.3, 2016.
URL : https://hal.archives-ouvertes.fr/inserm-01345616

K. J. Gorgolewski and R. A. Poldrack, A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research, PLoS Biol, vol.14, 2016.

H. T. Gorji and J. Haddadnia, A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI, Neuroscience, vol.305, pp.361-371, 2015.

K. A. Gunawardena, R. N. Rajapakse, and N. D. Kodikara, Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data, 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp.1-7, 2017.

C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, On Calibration of Modern Neural Networks, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.1321-1330, 2017.

B. Gutiérrez-becker and C. Wachinger, Deep Multi-structural Shape Analysis: Application to Neuroanatomy: 21st International Conference, pp.523-531, 2018.

S. Haller, K. O. Lovblad, and P. Giannakopoulos, Principles of classification analyses in mild cognitive impairment (MCI) and Alzheimer disease, J. Alzheimers. Dis, vol.26, pp.389-394, 2011.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.

M. Hon and N. M. Khan, Towards Alzheimer's disease classification through transfer learning, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1166-1169, 2017.

E. Hosseini-asl, M. Ghazal, A. Mahmoud, A. Aslantas, A. Shalaby et al., Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network, Front. Biosci, vol.23, pp.584-596, 2018.

E. Hosseini-asl, R. Keynton, and A. El-baz, Alzheimer's disease diagnostics by adaptation of 3D convolutional network, 2016 IEEE International Conference on Image Processing (ICIP), pp.126-130, 2016.

J. Islam and Y. Zhang, Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks, Brain Inform, vol.5, 2018.

J. Islam, Y. Zhang, Y. Zeng, Y. He, J. H. Kotaleski et al., A Novel Deep Learning Based Multi-class Classification Method for Alzheimer's Disease Detection Using Brain MRI Data, Lecture Notes in Computer Science, pp.213-222, 2017.

D. Jha, J. Kim, and G. Kwon, Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network, J. Healthc. Eng, 2017.

S. Korolev, A. Safiullin, M. Belyaev, and Y. Dodonova, Residual and plain convolutional neural networks for 3D brain MRI classification, IEEE 14th International Symposium on Biomedical Imaging (ISBI), pp.835-838, 2017.

N. Kriegeskorte, W. K. Simmons, P. S. Bellgowan, and C. I. Baker, Circular analysis in systems neuroscience: the dangers of double dipping, Nat. Neurosci, vol.12, pp.535-540, 2009.

A. Krizhevsky, I. Sutskever, G. E. Hinton, F. Pereira, C. J. Burges et al., ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012.

M. Kuhn and K. Johnson, Applied Predictive Modeling, 2013.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, pp.436-444, 2015.

C. Ledig, R. A. Heckemann, A. Hammers, J. C. Lopez, V. F. Newcombe et al., Robust whole-brain segmentation: application to traumatic brain injury, Med. Image Anal, vol.21, pp.40-58, 2015.

C. Lian, M. Liu, J. Zhang, and D. Shen, Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI, IEEE Trans. Pattern Anal. Mach. Intell, 2018.

F. Li, D. Cheng, and M. Liu, Alzheimer's disease classification based on combination of multi-model convolutional networks, IEEE International Conference on Imaging Systems and Techniques (IST), pp.1-5, 2017.

F. Li and M. Liu, Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks, Comput. Med. Imaging Graph, vol.70, pp.101-110, 2018.

F. Li, L. Tran, K. Thung, S. Ji, D. Shen et al., A Robust Deep Model for Improved Classification of AD/MCI Patients, IEEE J Biomed Health Inform, vol.19, pp.1610-1616, 2015.

W. Lin, T. Tong, Q. Gao, D. Guo, X. Du et al., Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment, Front. Neurosci, vol.12, 2018.

J. Liu, Y. Pan, M. Li, Z. Chen, L. Tang et al., Applications of deep learning to MRI images: A survey, Big Data Mining and Analytics, vol.1, pp.1-18, 2018.

J. Liu, S. Shang, K. Zheng, and J. Wen, Multi-view ensemble learning for dementia diagnosis from neuroimaging: An artificial neural network approach, Neurocomputing, vol.195, pp.112-116, 2016.

M. Liu, D. Cheng, K. Wang, and Y. Wang, Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis, Neuroinformatics, vol.16, pp.295-308, 2018.

M. Liu, J. Zhang, E. Adeli, and D. Shen, Landmark-based deep multi-instance learning for brain disease diagnosis, Med. Image Anal, vol.43, pp.157-168, 2018.

M. Liu, J. Zhang, E. Adeli, and D. Shen, Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis, IEEE Trans. Biomed. Eng, 2018.

M. Liu, J. Zhang, D. Nie, P. Yap, and D. Shen, Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis, IEEE J Biomed Health Inform, vol.22, pp.1476-1485, 2018.

S. Liu, S. Liu, W. Cai, H. Che, S. Pujol et al., Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease, IEEE Trans. Biomed. Eng, vol.62, pp.1132-1140, 2015.

D. Lu, K. Popuri, G. W. Ding, R. Balachandar, and M. F. Beg, Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images, Sci. Rep, vol.8, 2018.

A. S. Lundervold and A. Lundervold, An overview of deep learning in medical imaging focusing on MRI, p.40, 2018.

, Z. Med. Phys

B. S. Mahanand, S. Suresh, N. Sundararajan, and M. Kumar, Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network, Neural Netw, vol.32, pp.313-322, 2012.

M. Maitra and A. Chatterjee, A Slantlet transform based intelligent system for magnetic resonance brain image classification, Biomed. Signal Process. Control, vol.1, pp.299-306, 2006.

D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris et al., Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, J. Cogn. Neurosci, vol.19, pp.1498-1507, 2007.

N. A. Mathew, R. S. Vivek, and P. R. Anurenjan, Early Diagnosis of Alzheimer's Disease from MRI Images Using PNN, International CET Conference on Control, Communication, and Computing (IC4), pp.161-164, 2018.

G. Mckhann, D. Drachman, M. Folstein, R. Katzman, D. Price et al., Clinical diagnosis of Alzheimer's disease Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology, vol.34, pp.939-939, 1984.

M. Mostapha, S. Kim, G. Wu, L. Zsembik, S. Pizer et al., Non-Euclidean, convolutional learning on cortical brain surfaces, IEEE 15th International Symposium on Biomedical Imaging (ISBI, pp.527-530, 2018.

K. Ning, B. Chen, F. Sun, Z. Hobel, L. Zhao et al., Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework, Neurobiol. Aging, vol.68, pp.151-158, 2018.

A. Ortiz, J. Munilla, J. M. Górriz, and J. Ramírez, Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease, Int. J. Neural Syst, vol.26, 2016.

S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. Guerrero et al., Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease, Med. Image Anal, vol.48, pp.117-130, 2018.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in PyTorch, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst et al., Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization, Neurology, vol.74, pp.201-209, 2010.

R. A. Poldrack, C. I. Baker, J. Durnez, K. J. Gorgolewski, P. M. Matthews et al., Scanning the horizon: towards transparent and reproducible neuroimaging research, Nat. Rev. Neurosci, vol.18, pp.115-126, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01896468

S. Qiu, G. H. Chang, M. Panagia, D. M. Gopal, R. Au et al., Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment, Assessment & Disease Monitoring, vol.10, pp.737-749, 2018.

P. R. Raamana, neuropredict: easy machine learning and standardized predictive analysis of biomarkers, 2017.

S. Raschka, Python Machine Learning, 2015.

S. Rathore, M. Habes, M. A. Iftikhar, A. Shacklett, and C. Davatzikos, A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages, Neuroimage, vol.155, pp.530-548, 2017.

A. Raut and V. Dalal, A machine learning based approach for detection of alzheimer's disease using analysis of hippocampus region from MRI scan, International Conference on Computing Methodologies and Communication (ICCMC), pp.236-242, 2017.

,

M. I. Razzak, S. Naz, and A. Zaib, Deep Learning for Medical Image Processing: Overview, Challenges and the Future, pp.323-350, 2018.

B. D. Ripley, Pattern Recognition and Neural Networks by, 1996.

A. Routier, J. Guillon, N. Burgos, J. Samper-gonzález, J. Wen et al., Clinica: an open source software platform for reproducible clinical neuroscience studies, Annual Meeting of the Organization for Human Brain Mapping (OHBM), 2018.
URL : https://hal.archives-ouvertes.fr/hal-02308126

C. Salvatore, A. Cerasa, P. Battista, M. C. Gilardi, A. Quattrone et al., Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach, Front. Neurosci, vol.9, 2015.

J. Samper-gonzález, N. Burgos, S. Bottani, S. Fontanella, P. Lu et al., Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data, 2018.

W. S. Sarle, Neural Network FAQ, part 1 of 7. Introduction, periodic posting to the Usenet newsgroup comp. ai. neural-nets URL, 1997.

N. Schuff, N. Woerner, L. Boreta, T. Kornfield, L. M. Shaw et al., MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers, Brain, vol.132, pp.1067-1077, 2009.

U. Senanayake, A. Sowmya, and L. Dawes, Deep fusion pipeline for mild cognitive impairment diagnosis, IEEE 15th International Symposium on Biomedical Imaging, pp.1394-1997, 2018.

A. Shams-baboli and M. Ezoji, A Zernike moment based method for classification of Alzheimer's disease from structural MRI, 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), pp.38-43, 2017.

T. Shen, J. Jiang, Y. Li, P. Wu, C. Zuo et al., Decision Supporting Model for One-year Conversion Probability from MCI to AD using CNN and SVM, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.738-741, 2018.

J. Shi, X. Zheng, Y. Li, Q. Zhang, and S. Ying, Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease, IEEE J Biomed Health Inform, vol.22, pp.173-183, 2018.

Y. Shmulev, M. Belyaev, D. Stoyanov, Z. Taylor, E. Ferrante et al., Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging: Second International Workshop, GRAIL 2018 and First International Workshop, Beyond MIC 2018, Held in Conjunction with MICCAI, Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities, pp.83-91, 2018.

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.

S. Sonnenburg, M. L. Braun, C. S. Ong, S. Bengio, L. Bottou et al., The Need for Open Source Software in Machine Learning, J. Mach. Learn. Res, vol.8, pp.2443-2466, 2007.

S. E. Spasov, L. Passamonti, A. Duggento, P. Lio, and N. Toschi, , 2018.

, Network Framework for the Prediction of Alzheimer's Disease, Conf. Proc. IEEE Eng. Med. Biol. Soc, pp.1271-1274, 2018.

V. Stodden, F. Leisch, and R. D. Peng, Implementing Reproducible Research, 2014.

H. Suk, S. Lee, and D. Shen, Deep ensemble learning of sparse regression models for brain disease diagnosis, Med. Image Anal, vol.37, pp.101-113, 2017.

H. Suk, S. Lee, and D. Shen, Latent feature representation with stacked auto-encoder for AD/MCI diagnosis, Brain Struct. Funct, vol.220, pp.841-859, 2015.

H. Suk, S. Lee, and D. Shen, Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis, Neuroimage, vol.101, pp.569-582, 2014.

A. M. Taqi, A. Awad, F. Al-azzo, and M. Milanova, The Impact of Multi-Optimizers and Data Augmentation on TensorFlow Convolutional Neural Network Performance, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp.140-145, 2018.

K. Thung, P. Yap, and D. Shen, Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 10553, pp.160-168, 2017.

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4ITK: improved N3 bias correction, IEEE Trans. Med. Imaging, vol.29, pp.1310-1320, 2010.

A. Valliani and A. Soni, Deep Residual Nets for Improved Alzheimer's Diagnosis, 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, pp.615-615, 2017.

J. Vanschoren, J. N. Van-rijn, B. Bischl, and L. Torgo, OpenML: Networked Science in Machine Learning, SIGKDD Explor. Newsl, vol.15, pp.49-60, 2014.

T. Vu, N. Ho, H. Yang, J. Kim, and H. Song, Non-white matter tissue extraction and deep convolutional neural network for Alzheimer's disease detection, Soft Comput, vol.22, pp.6825-6833, 2018.

T. D. Vu, H. Yang, V. Q. Nguyen, A. Oh, and M. Kim, Multimodal learning using Convolution Neural Network and Sparse Autoencoder, IEEE International Conference on Big Data and Smart Computing (BigComp), pp.309-312, 2017.

H. Wang, Y. Shen, S. Wang, T. Xiao, L. Deng et al., Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer's disease, Neurocomputing, vol.333, pp.145-156, 2019.

S. Wang, P. Phillips, Y. Sui, B. Liu, M. Yang et al., Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling, J. Med. Syst, vol.42, 2018.

S. Wang, Y. Shen, W. Chen, T. Xiao, and J. Hu, Automatic Recognition of Mild Cognitive Impairment from MRI Images Using Expedited Convolutional Neural Networks, Artificial Neural Networks and Machine Learning -ICANN 2017, pp.373-380, 2017.

X. Wang, W. Cai, D. Shen, and H. Huang, Temporal Correlation Structure Learning for MCI Conversion Prediction, 21st International Conference, pp.446-454, 2018.

D. Wen, Z. Wei, Y. Zhou, G. Li, X. Zhang et al., Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion, Front. Neuroinform, vol.12, 2018.

J. Wen, J. Samper-gonzalez, S. Bottani, A. Routier, N. Burgos et al., Reproducible evaluation of diffusion MRI features for automatic classification of patients, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02566361

C. Wu, S. Guo, Y. Hong, B. Xiao, Y. Wu et al., Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant, Imaging Med. Surg, vol.8, pp.992-1003, 2018.

Y. Zhang, S. Wang, Y. Sui, M. Yang, B. Liu et al., Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization, J. Alzheimers. Dis, vol.65, pp.855-869, 2018.

T. Zhou, K. Thung, X. Zhu, and D. Shen, Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis, Hum. Brain Mapp, vol.40, pp.1001-1016, 2019.

T. Zhou, K. Thung, X. Zhu, and D. Shen, Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis, Mach Learn Med Imaging, vol.10541, pp.132-140, 2017.

. Avants, also offer solutions for both linear and non-linear registration, 1995.

, In the following, we briefly describe these datasets and provide explanations on the diagnosis labels provided. Indeed, the diagnostic criteria of these studies differ, hence there is no strict equivalence between the labels of ADNI and AIBL, and those of OASIS, the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Australian Imaging

. Petersen, For up-to-date information, see www.adni-info.org.1 The ADNI study is composed of 4 cohorts: ADNI-1, ADNI-GO, ADNI-2 and ADNI-3. These cohorts are dependant and longitudinal, meaning that one cohort may include the same patient more than once and that different cohorts may include the same patients. Diagnosis labels are given by a physician after a series of tests, The ADNI was launched in 2003 as a public-private partnership, 2010.

. Aisen, hence the MCI label has been split into two labels: -EMCI (early MCI): patients at the beginning of the prodromal phase, -LMCI (late MCI): patients at the end of the prodromal phase, Since the ADNI-GO and ADNI-2 cohorts, new patients at the very beginning of the prodromal stage have been recruited, 2010.

, This division is made on the basis of the score obtained on memory tasks corrected by the education level. However, both classes remain very similar and they are fused in many studies under the MCI label. ? Australian Imaging, Biomarkers and Lifestyle

. Ellis, Biomarker & Lifestyle Flagship Study of Ageing seeks to discover which biomarkers, cognitive characteristics, and health and lifestyle factors determine the development of AD. The AIBL project includes a longitudinal cohort of patients. Several modalities are present in the dataset, such as clinical and imaging (MRI and PET) data, as well as the analysis of blood and CSF samples. As in ADNI, the diagnosis is given according to a series of clinical tests, We also used data collected by the AIBL study group. Similarly to ADNI, the Australian Imaging, 2009.

. Marcus, Two labels can be found in the OASIS-1 dataset: -AD, which corresponds to patients with a non-null CDR score. This class gathers patients who would be spread between the MCI and AD classes in ADNI, Diagnosis labels are given only based on the clinical dementia rating (CDR) scale, 2007.

, CDR, the scores of 0.5, 1, 2 and 3 representing very mild, mild, moderate and severe dementia, respectively. -Control

P. S. Aisen, R. C. Petersen, M. C. Donohue, A. Gamst, R. Raman et al., Clinical Core of the Alzheimer's Disease Neuroimaging Initiative: progress and plans, Alzheimers. Dement, vol.6, pp.239-246, 2010.

J. L. Andersson, M. Jenkinson, and S. Smith, Non-linear registration aka Spatial normalisation, 2010.

J. Ashburner and K. J. Friston, Voxel-Based Morphometry-The Methods, Neuroimage, vol.11, pp.805-821, 2000.

B. B. Avants, N. J. Tustison, M. Stauffer, G. Song, B. Wu et al., The Insight ToolKit image registration framework, Front. Neuroinform, vol.8, p.44, 2014.

H. Bourlard and Y. Kamp, Auto-association by multilayer perceptrons and singular value decomposition, Biol. Cybern, vol.59, pp.291-294, 1988.

D. C. Ciresan, U. Meier, J. Masci, L. Maria-gambardella, and J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, IJCAI Proceedings-International Joint Conference on Artificial Intelligence, p.1237, 2011.

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy et al., Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database, Neuroimage, vol.56, pp.766-781, 2011.

,. De-boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, A Tutorial on the Cross-Entropy Method, Ann. Oper. Res, vol.134, pp.19-67, 2005.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition, 2009.

V. Dumoulin and F. Visin, A guide to convolution arithmetic for deep learning, 2016.

K. A. Ellis, A. I. Bush, D. Darby, D. De-fazio, J. Foster et al., The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease, Int. Psychogeriatr, vol.21, pp.672-687, 2009.

K. A. Ellis, C. C. Rowe, V. L. Villemagne, R. N. Martins, C. L. Masters et al., Addressing population aging and Alzheimer's disease through the Australian Imaging Biomarkers and Lifestyle study: Collaboration with the Alzheimer's Disease Neuroimaging Initiative, 2010.

D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent et al., Why Does Unsupervised Pre-training Help Deep Learning?, J. Mach. Learn. Res, vol.11, pp.625-660, 2010.

K. J. Friston, A. P. Holmes, J. B. Poline, P. J. Grasby, S. C. Williams et al., Analysis of fMRI time-series revisited, Neuroimage, vol.2, pp.45-53, 1995.

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

I. Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks. arXiv, 2016.

I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning, 2016.

D. N. Greve and B. Fischl, Accurate and robust brain image alignment using boundary-based registration, Neuroimage, vol.48, pp.63-72, 2009.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.

G. E. Hinton and R. S. Zemel, Autoencoders, Minimum Description Length and Helmholtz Free Energy, Advances in Neural Information Processing Systems, vol.6, pp.3-10, 1994.

G. Huang, Z. Liu, L. Van-der-maaten, and K. Q. Weinberger, Densely connected convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4700-4708, 2017.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015.

K. Janocha and W. M. Czarnecki, On Loss Functions for Deep Neural Networks in Classification, 2017.

M. Jenkinson, P. Bannister, M. Brady, and S. Smith, Improved optimization for the robust and accurate linear registration and motion correction of brain images, Neuroimage, vol.17, pp.825-841, 2002.

M. Jenkinson and S. Smith, A global optimisation method for robust affine registration of brain images, Med. Image Anal, vol.5, pp.143-156, 2001.

P. Juszczak, D. Tax, and R. P. Duin, Feature scaling in support vector data description, Proc. ASCI. Citeseer, pp.95-102, 2002.

P. Kalavathi and V. B. Prasath, Methods on Skull Stripping of MRI Head Scan Images-a Review, J. Digit. Imaging, vol.29, pp.365-379, 2016.

A. Krizhevsky, I. Sutskever, G. E. Hinton, F. Pereira, C. J. Burges et al., ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012.

A. Krogh and J. A. Hertz, A Simple Weight Decay Can Improve Generalization, Advances in Neural Information Processing Systems, vol.4, pp.950-957, 1992.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, vol.86, pp.2278-2324, 1998.

D. Lu and Q. Weng, A survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens, vol.28, pp.823-870, 2007.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, Rectifier nonlinearities improve neural network acoustic models, Proc. Icml, p.3, 2013.

A. Madabhushi and J. K. Udupa, Interplay between intensity standardization and inhomogeneity correction in MR image processing, IEEE Trans. Med. Imaging, vol.24, pp.561-576, 2005.

D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris et al., Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults, J. Cogn. Neurosci, vol.19, pp.1498-1507, 2007.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.807-814, 2010.

F. P. Oliveira and J. M. Tavares, Medical image registration: a review, Comput. Methods Biomech. Biomed. Engin, vol.17, pp.73-93, 2014.

A. Panigrahi, Y. Chen, -. Kuo, and C. , ANALYSIS ON GRADIENT PROPAGATION IN BATCH NORMALIZED RESIDUAL NETWORKS, 2018.

L. Perez and J. Wang, The Effectiveness of Data Augmentation in Image Classification using Deep Learning, 2017.

R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst et al., Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization, Neurology, vol.74, pp.201-209, 2010.

L. Prechelt, G. Montavon, and G. B. Orr, Early Stopping -But When?, Neural Networks: Tricks of the Trade: Second Edition, pp.53-67, 2012.

D. Scherer, A. Müller, and S. Behnke, Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition, Artificial Neural Networks -ICANN 2010, pp.92-101, 2010.

D. W. Shattuck, S. R. Sandor-leahy, K. A. Schaper, D. A. Rottenberg, and R. M. Leahy, Magnetic resonance image tissue classification using a partial volume model, Neuroimage, vol.13, pp.856-876, 2001.

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.

J. G. Sled, A. P. Zijdenbos, and A. C. Evans, A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Trans. Med. Imaging, vol.17, pp.87-97, 1998.

S. M. Smith, Fast robust automated brain extraction, Hum. Brain Mapp, vol.17, pp.143-155, 2002.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res, vol.15, pp.1929-1958, 2014.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., , p.23

A. , Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4ITK: improved N3 bias correction, IEEE Trans. Med. Imaging, vol.29, pp.1310-1320, 2010.

S. Uchida, Image processing and recognition for biological images, Dev. Growth Differ, vol.55, pp.523-549, 2013.

U. Vovk, F. Pernus, and B. Likar, A review of methods for correction of intensity inhomogeneity in MRI, IEEE Trans. Med. Imaging, vol.26, pp.405-421, 2007.

L. Yann, Modeles connexionnistes de lapprentissage, 1987.

Y. Yao, L. Rosasco, and A. Caponnetto, On Early Stopping in Gradient Descent Learning, Constr. Approx, vol.26, pp.289-315, 2007.

T. Zhang and B. Yu, Boosting with early stopping: Convergence and consistency, Ann. Stat, vol.33, pp.1538-1579, 2005.