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awesome-deepneuroimage

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A curated list of awesome deep learning applications in the field of neurological image analysis

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# Awesome DeepNeuroImage [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/alxndrkalinin/awesome-deepneuroimage)

A curated list of awesome deep learning applications in the field of neurological image analysis

- **2013-06** | Natural Image Bases to Represent Neuroimaging Data | *Ashish Gupta, Murat Seckin Ayhan, Anthony S. Maida* | [Proceedings of the 30th International Conference on Machine Learning (PMLR)](http://proceedings.mlr.press/v28/gupta13b.html)

- **2013-09** | Manifold learning of brain MRIs by deep learning | *Brosch, Tom, Roger Tam, and Alzheimer’s Disease Neuroimaging Initiative* | [2013 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)](http:/dx.doi.org/10.1007/978-3-642-40763-5_78)

- **2013-09** | Unsupervised deep feature learning for deformable registration of MR brain images | *Wu, Guorong, Minjeong Kim, Qian Wang, Yaozong Gao, Shu Liao, and Dinggang Shen* | [2013 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)](http:/dx.doi.org/10.1007/978-3-642-40763-5_80)

- **2013-09** | Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images | *Kim, Minjeong, Guorong Wu, and Dinggang Shen* | [2013 International Workshop on Machine Learning in Medical Imaging](http:/dx.doi.org/10.1007/978-3-319-02267-3_1)

- **2014-05** | High-level feature based PET image retrieval with deep learning architecture | *Liu, Siqi, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, Michael Fulham, and Dagan Feng* | [Journal of Nuclear Medicine](http://jnm.snmjournals.org/content/55/supplement_1/2028)

- **2014-08** | Deep learning for neuroimaging: a validation study | *Plis, Sergey M., Devon R. Hjelm, Ruslan Salakhutdinov, Elena A. Allen, Henry J. Bockholt, Jeffrey D. Long, Hans J. Johnson, Jane S. Paulsen, Jessica A. Turner, and Vince D. Calhoun* | [Frontiers in Neuroscience](http://dx.doi.org/10.3389/fnins.2014.00229)

- **2014-08** | Restricted Boltzmann Machines for Neuroimaging: an Application in Identifying Intrinsic Networks | *Hjelm, R. Devon, Vince D. Calhoun, Ruslan Salakhutdinov, Elena A. Allen, Tulay Adali, and Sergey M. Plis* | [NeuroImage](http://dx.doi.org/10.1016/j.neuroimage.2014.03.048)

- **2014-09** | Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning | *Brosch, Tom, Youngjin Yoo, David KB Li, Anthony Traboulsee, and Roger Tam* | [2014 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)](http://dx.doi.org/10.1007/978-3-319-10470-6_58)

- **2014-09** | Deep learning of image features from unlabeled data for multiple sclerosis lesion segmentation | *Yoo, Youngjin, Tom Brosch, Anthony Traboulsee, David KB Li, and Roger Tam* | [2014 International Workshop on Machine Learning in Medical Imaging](http://dx.doi.org/10.1007/978-3-319-10470-6_58)

- **2014-09** | Segmenting Hippocampus from Infant Brains by Sparse Patch Matching with Deep-Learned Features | *Guo, Yanrong, Guorong Wu, Leah A. Commander, Stephanie Szary, Valerie Jewells, Weili Lin, and Dinggang Shen* | [2014 International Conference on Medical Image Computing and Computer-Assisted Intervention](http://dx.doi.org/10.1007/978-3-319-10470-6_39)

- **2014-09** | Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis | *Rongjian Li, Wenlu Zhang, Heung-Il Suk, Li Wang, Jiang Li, Dinggang Shen, Shuiwang Ji*| [Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014)](https://doi.org/10.1007/978-3-319-10443-0_39)

- **2014-10** | Deep learning for brain decoding | *Firat, Orhan, Like Oztekin, and Fatos T. Yarman Vural* | [IEEE International Conference on Image Processing (ICIP)](http://dx.doi.org/10.1109/ICIP.2014.7025563)

- **2014-11** | Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis | *Suk, Heung-Il, Seong-Whan Lee, Dinggang Shen, and Alzheimer's Disease Neuroimaging Initiative* | [NeuroImage](http://dx.doi.org/10.1016/j.neuroimage.2014.06.077)

- **2014-11** | Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease | *Liu, Siqi, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, Dagan Feng, and Michael J. Fulham* | [IEEE Transactions on Biomedical Engineering](http://dx.doi.org/10.1109/TBME.2014.2372011)

- **2014-11** | Classification on ADHD with Deep Learning | *Kuang, Deping, and Lianghua He* | [2014 International Conference on Cloud Computing and Big Data (CCBD)](http://dx.doi.org/10.1109/CCBD.2014.42)

- **2014-12** | Learning Deep Temporal Representations for Brain Decoding | *Firat, Orhan, Emre Aksan, Ilke Oztekin, and Fatos T. Yarman Vural* | [arXiv](http://arxiv.org/abs/1412.7522)

- **2015-01** | Deep learning of fMRI big data: a novel approach to subject-transfer decoding | *Koyamada, Sotetsu, Yumi Shikauchi, Ken Nakae, Masanori Koyama, and Shin Ishii* | [arXiv](https://arxiv.org/abs/1502.00093)

- **2015-02** | Multi-Phase Feature Representation Learning for Neurodegenerative Disease Diagnosis | *Liu, Siqi, Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis, and David Dagan Feng* | [2015 Australasian Conference on Artificial Life and Computational Intelligence](http://dx.doi.org/10.1007/978-3-319-14803-8_27)

- **2015-02** | Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks | *Adrien Payan, Giovanni Montana* | [arXiv](https://arxiv.org/abs/1502.02506)

- **2015-03** | Latent feature representation with stacked auto-encoder for AD/MCI diagnosis | *Suk, Heung-Il, Seong-Whan Lee, Dinggang Shen, and Alzheimer’s Disease Neuroimaging Initiative* | [Brain Structure and Function](http://dx.doi.org/10.1007/s00429-013-0687-3)

- **2015-03** | Deep convolutional neural networks for multi-modality isointense infant brain image segmentation | *Zhang, Wenlu, Rongjian Li, Houtao Deng, Li Wang, Weili Lin, Shuiwang Ji, and Dinggang Shen* | [NeuroImage](http://dx.doi.org/10.1016/j.neuroimage.2014.12.061)

- **2015-05** | A Robust Deep Model for Improved Classification of AD/MCI Patients | *Feng Li, Loc Tran, Kim-Han Thung, Shuiwang Ji, Dinggang Shen, and Jiang Li* | [IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS](https://doi.org/10.1109/JBHI.2015.2429556)

- **2015-06** | Deep neural networks for anatomical brain segmentation | *de Brebisson, Alexander, and Giovanni Montana* | [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops](http://dx.doi.org/10.1109/CVPRW.2015.7301312)

- **2015-09** | Deep independence network analysis of structural brain imaging: A simulation study | *Castro, Eduardo, Devon Hjelm, Sergey Plis, Laurent Dinh, Jessica Turner, and Vince Calhoun* | [2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)](http://dx.doi.org/10.1109/MLSP.2015.7324318)

- **2015-10** | Multi-Scale 3D Convolutional Neural Networks for Lesion Segmentation in Brain MRI | *Kamnitsas, Konstantinos, Liang Chen, Christian Ledig, Daniel Rueckert, and Ben Glocker* | [2015 Ischemic Stroke Lesion Segmentation Challenge (ISLES)](http://www.isles-challenge.org/ISLES2015/pdf/20150930_ISLES2015_Proceedings.pdf#page=21)

- **2015-10** | Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing | *Ghesu, Florin C., Bogdan Georgescu, Yefeng Zheng, Joachim Hornegger, and Dorin Comaniciu* | [2015 International Conference on Medical Image Computing and Computer-Assisted Intervention](http://dx.doi.org/10.1007/978-3-319-24553-9_87)

- **2015-11** | Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation | *Brosch, Tom, Youngjin Yoo, Lisa YW Tang, David KB Li, Anthony Traboulsee, and Roger Tam* | [2015 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)](http://dx.doi.org/10.1007/978-3-319-24574-4_1)

- **2016-01** | Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia | *Kim, Junghoe, Vince D. Calhoun, Eunsoo Shim, and Jong-Hwan Lee* | [NeuroImage](http://dx.doi.org/10.1016/j.neuroimage.2015.05.018)

- **2016-04** | Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation | *Kamnitsas, Konstantinos, Christian Ledig, Virginia FJ Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, and Ben Glocker* | [arXiv](https://arxiv.org/abs/1603.05959)

- **2016-04** | Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach | *Amin, Md Faijul, Sergey M. Plis, Eswar Damaraju, Devon Hjelm, KyungHyun Cho, and Vince D. Calhoun* | [2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)](http://dx.doi.org/10.1109/SSIAI.2016.7459160)

- **2016-04** | Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks | *Jang, Hojin, Sergey M. Plis, Vince D. Calhoun, and Jong-Hwan Lee* | [NeuroImage](http://dx.doi.org/10.1016/j.neuroimage.2016.04.003)

- **2016-05** | Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation | *Brosch, Tom, Lisa YW Tang, Youngjin Yoo, David KB Li, Anthony Traboulsee, and Roger Tam* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2528821)

- **2016-05** | Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing | *Ghesu, Florin C., Edward Krubasik, Bogdan Georgescu, Vivek Singh, Yefeng Zheng, Joachim Hornegger, and Dorin Comaniciu* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2538802)

- **2016-05** | q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans | *Golkov, Vladimir, Alexey Dosovitskiy, Jonathan I. Sperl, Marion I. Menzel, Michael Czisch, Philipp Sämann, Thomas Brox, and Daniel Cremers* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2551324)

- **2016-05** | Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks | *Dou, Qi, Hao Chen, Lequan Yu, Lei Zhao, Jing Qin, Defeng Wang, Vincent CT Mok, Lin Shi, and Pheng-Ann Heng* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2528129)

- **2016-05** | Automatic Segmentation of MR Brain Images With a Convolutional Neural Network | *Moeskops, Pim, Max A. Viergever, Adriënne M. Mendrik, Linda S. de Vries, Manon JNL Benders, and Ivana Išgum* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2548501)

- **2016-05** | Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images | *Pereira, Sérgio, Adriano Pinto, Victor Alves, and Carlos A. Silva* | [IEEE transactions on medical imaging](http://dx.doi.org/10.1109/TMI.2016.2538465)

- **2016-05** | Clinical decision support for Alzheimer's disease based on deep learning and brain network | *Chenhui Hu, Ronghui Ju, Yusong Shen, Pan Zhou, Li., Q* | [2016 IEEE International Conference on Communications (ICC)](https://doi.org/10.1109/ICC.2016.7510831)

- **2016-06** | Longitudinal Brain Structure Changes in Healthy/Mci Patients: A Deep Learning Approach for the Diagnosis and Prognosis of Alzheimer's Disease | *Peng Dai, Femida Gwadry-Sridhar, Michael Bauer, Michael Borrie, Xue Teng* | [Alzheimer's & Dementia](https://doi.org/10.1016/j.jalz.2016.06.1063)

- **2016-07** | Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia | *Castro, Eduardo, R. Devon Hjelm, Sergey Plis, Laurent Dihn, Jessica Turner, and Vince Calhoun* | [IEEE Transactions on Medical Imaging](http://dx.doi.org/10.1109/TMI.2016.2527717)

- **2016-07** | Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network | *Ehsan Hosseini-Asl, Robert Keynto, Ayman El-Baz* | [2016 IEEE International Conference on Image Processing](http://dx.doi.org/10.1109/icip.2016.7532332)

- **2016-08** | VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation | *Chen, Hao, Qi Dou, Lequan Yu, and Pheng-Ann Heng* | [arXiv](https://arxiv.org/abs/1608.05895)

- **2016-10** | Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis. | *Yoo, Youngjin, Lisa W. Tang, Tom Brosch, David KB Li, Luanne Metz, Anthony Traboulsee, and Roger Tam* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_10)

- **2016-10** | Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features | *Bahrami, Khosro, Feng Shi, Islem Rekik, and Dinggang Shen* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_5)

- **2016-10** | Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks | *Birenbaum, Ariel, and Hayit Greenspan* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_7)

- **2016-10** | De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks | *Benou, Ariel, Ronel Veksler, Alon Friedman, and Tammy Riklin Raviv* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_11)

- **2016-10** | Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data | *Andermatt, Simon, Simon Pezold, and Philippe Cattin* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_15)

- **2016-10** | Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes | *Hoffmann, Nico, Edmund Koch, Gerald Steiner, Uwe Petersohn, and Matthias Kirsch* | [Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings](http://dx.doi.org/10.1007/978-3-319-46976-8_16)

- **2016-10** | 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients | *Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen* | [Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016)](https://doi.org/10.1007/978-3-319-46723-8_25)

- **2016-12** | Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker | *James H Cole, Rudra PK Poudel, Dimosthenis Tsagkrasoulis, Matthan WA Caan, Claire Steves, Tim D Spector, Giovanni Montana* | [arXiv](https://arxiv.org/abs/1612.02572)

- **2016-12** | DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI | *Saman Sarraf, John Anderson, Ghassem Tofighi* | [bioRxiv](http://www.biorxiv.org/content/early/2016/12/23/070441)

- **2017-01** | Brain tumor segmentation with Deep Neural Networks | *Havaei, Mohammad, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, and Hugo Larochelle* | [Medical Image Analysis](http://dx.doi.org/10.1016/j.media.2016.05.004)

- **2017-01** | Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification | *S. Korolev., A. Safiulliny., M. Belyaev., and Y. Dodonova.* | [arXiv](https://arxiv.org/abs/1701.06643)

- **2017-02** | BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment | *J. Kawahara et al.* | [Neuroimage](https://doi.org/10.1016/j.neuroimage.2016.09.046)

- **2017-02** | Generic decoding of seen and imagined objects using hierarchical visual features | *T Horikawa, Y Kamitani* | [Nat Commun](https://doi.org/10.1038/ncomms15037)

- **2017-02** | DeepNAT: Deep convolutional neural network for segmenting neuroanatomy | *Christian Wachingera, Martin Reuterb, Tassilo Klein* | [NeuroImage](https://doi.org/10.1016/j.neuroimage.2017.02.035)

- **2017-04** | Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging | *Hongyoon Choi, Kyong Hwan Jin* | [arXiv](https://arxiv.org/abs/1704.06033)

- **2017-03** | Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications | *Sandra Vieira, Walter H.L. Pinaya, Andrea Mechelli* | [Neuroscience & Biobehavioral Reviews](https://doi.org/10.1016/j.neubiorev.2017.01.002)

- **2017-04** | Deep ensemble learning of sparse regression models for brain disease diagnosis | *Heung-Il Suk, Seong-Whan Leea, Dinggang Shen* | [Medical Image Analysis](https://doi.org/10.1016/j.media.2017.01.008)

- **2017-04** | VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images | *H. Chen, Q. Dou, L. Yu, J. Qin, and P. A. Heng* | [Neuroimage](https://doi.org/10.1016/j.neuroimage.2017.04.041)

- **2017-04** | A novel ensemble approach on regionalized neural networks for brain disorder prediction | *L. Zheng, J. Zhang, B. Cao, P. S. Yu, and A. Ragin* | [Proceedings of the Symposium on Applied Computing](https://doi.org/10.1145/3019612.3019668)

- **2017-06** | FuseMe: Classification of sMRI images by fusion of Deep CNNs on 2D+epsilon projections | *K. ADERGHAL., J. BENOIS-PINEAU., K. AFDEL., and C. GWENAËLLE* | [International Workshop on Content-based Multimedia Indexing](https://doi.org/10.1145/3095713.3095749)

- **2017-07** | Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker | *J. H. Cole et al.* | [Neuroimage](https://doi.org/10.1016/j.neuroimage.2017.07.059)

- **2017-10** | Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision | *H. Wen., J. Shi., Y. Zhang., K.-H. Lu., and Z. Liu.* | [Cerebral Cortex](https://doi.org/10.1093/cercor/bhx268)

- **2017-10** | Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks | *J. Zhang, M. X. Liu, and D. G. Shen* | [IEEE TIP](https://doi.org/10.1109/TIP.2017.2721106)

- **2017-12** | DeepRadiologyNet: Radiologist Level Pathology Detection in CT Head Images | *J. Merkowa., R. Lufkina., K. Nguyena., S. Soattob., Z. Tuc., and A. Vedaldi.* | [arXiv](https://arxiv.org/abs/1711.09313)

- **2018-01** | 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies | *Alexander Khvostikov, Karim Aderghal, Jenny Benois-Pineau, Andrey Krylov, Gwenaelle Catheline* | [arXiv](https://arxiv.org/abs/1801.05968)

- **2018-02** | Deep Learning in Neuroradiology | *G. Zaharchuk, E. Gong, M. Wintermark, D. Rubin, and C. P. Langlotz* | [AJNR Am J Neuroradiol](https://doi.org/10.3174/ajnr.A5543)

- **2018-02** | Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI | *E. Gong, J. M. Pauly, M. Wintermark, and G. Zaharchuk* | [J Magn Reson Imaging](https://doi.org/10.1002/jmri.25970)

- **2018-03** | Medical Image Synthesis with Deep Convolutional Adversarial Networks | *D. Nie et al.* | [IEEE Transactions on Biomedical Engineering](https://doi.org/10.1109/TBME.2018.2814538)

- **2018-03** | 3D conditional generative adversarial networks for high-quality PET image estimation at low dose | *Y. Wang et al.* | [Neuroimage](https://www.sciencedirect.com/science/article/pii/S1053811918302507)

- **2018-04** | Alzheimer Classification with MR images: Exploration of CNN Performance Factors | *Viktor Wegmayr, Daniel Haziza* | [Conference on Medical Imaging with Deep Learning](https://openreview.net/forum?id=BkfSH6ojM)

- **2018-05** | Multiscale Deep Neural Networks based analysis of FDG-PET images for the Early Diagnosis of Alzheimer’s Disease | *D. Lu, K. Popuri, G. W. Ding, R. Balachandar, and M. F. Beg* | [Medical Image Analysis](https://doi.org/10.1016/j.media.2018.02.002)

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