Awesome DeepNeuroImage

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

• 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

• 2014-10 | 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

• 2015-03 | 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

• 2015-06 | 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

• 2016-07 | 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

• 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

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