README.md

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)

• 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)

• 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)

• 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

• 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

• 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

• 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

• 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)

• 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

• 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

• 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)

• 2014-10 | Deep learning for brain decoding | Firat, Orhan, Like Oztekin, and Fatos T. Yarman Vural | IEEE International Conference on Image Processing (ICIP)

• 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

• 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

• 2014-11 | Classification on ADHD with Deep Learning | Kuang, Deping, and Lianghua He | 2014 International Conference on Cloud Computing and Big Data (CCBD)

• 2014-12 | Learning Deep Temporal Representations for Brain Decoding | Firat, Orhan, Emre Aksan, Ilke Oztekin, and Fatos T. Yarman Vural | arXiv

• 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

• 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

• 2015-02 | Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks | Adrien Payan, Giovanni Montana | arXiv

• 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

• 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

• 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

• 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

• 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)

• 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)

• 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

• 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)

• 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

• 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

• 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)

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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)

• 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

• 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

• 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

• 2016-08 | VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation | Chen, Hao, Qi Dou, Lequan Yu, and Pheng-Ann Heng | arXiv

• 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

• 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

• 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

• 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

• 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

• 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

• 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)

• 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

• 2016-12 | DeepAD: Alzheimer's Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI | Saman Sarraf, John Anderson, Ghassem Tofighi | bioRxiv

• 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

• 2017-01 | Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification | S. Korolev., A. Safiulliny., M. Belyaev., and Y. Dodonova. | arXiv

• 2017-02 | BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment | J. Kawahara et al. | Neuroimage

• 2017-02 | Generic decoding of seen and imagined objects using hierarchical visual features | T Horikawa, Y Kamitani | Nat Commun

• 2017-02 | DeepNAT: Deep convolutional neural network for segmenting neuroanatomy | Christian Wachingera, Martin Reuterb, Tassilo Klein | NeuroImage

• 2017-04 | Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging | Hongyoon Choi, Kyong Hwan Jin | arXiv

• 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

• 2017-04 | Deep ensemble learning of sparse regression models for brain disease diagnosis | Heung-Il Suk, Seong-Whan Leea, Dinggang Shen | Medical Image Analysis

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 2018-02 | Deep Learning in Neuroradiology | G. Zaharchuk, E. Gong, M. Wintermark, D. Rubin, and C. P. Langlotz | AJNR Am J Neuroradiol

• 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

• 2018-03 | Medical Image Synthesis with Deep Convolutional Adversarial Networks | D. Nie et al. | IEEE Transactions on Biomedical Engineering

• 2018-03 | 3D conditional generative adversarial networks for high-quality PET image estimation at low dose | Y. Wang et al. | Neuroimage

• 2018-04 | Alzheimer Classification with MR images: Exploration of CNN Performance Factors | Viktor Wegmayr, Daniel Haziza | Conference on Medical Imaging with Deep Learning

• 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

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