Keywords
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.
Content published before 2002 is available via pay-per-view purchase only.
Subscribe:
Subscribe to Neuroimaging ClinicsReferences
- Mastering the game of Go without human knowledge.Nature. 2017; 550: 354-359
- Grandmaster level in StarCraft Ii using multi-agent reinforcement learning.Nature. 2019; 575: 350-354
Fridman L, Brown DE, Glazer M, et al. MIT autonomous vehicle technology study: Large-scale deep learning based analysis of driver behavior and interaction with automation. arXiv preprint arXiv:1711.06976 1 (2017).
- A deep learning model for pediatric patient risk stratification.Am J Manag Care. 2019; 25: e310-e315
- International evaluation of an AI system for breast cancer screening.Nature. 2020; 577: 89-94
- Attention is all you need.Adv Neural Inf Process Syst. 2017; : 5998-6008
Devlin J, Chang M.-W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805.
- Deep learning.MIT Press, Cambridge2016
Abadi M, Barham P, Chen, J, et al. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). Savanah (GA); 2016. p. 265–83.
- Keras.(Available at:)https://github.com/fchollet/keras(Accessed July 29, 2020)Date: 2015
- PyTorch: An imperative style, high-performance deep learning library.Adv Neural Inf Process Syst. 2019; 32: 8024-8035
Chen T, Li M, Li Y, et al. MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems, arXiv preprint arXiv:1512.01274.
JiaY, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding, arXiv preprint arXiv:1408.5093.
- Big data: the next frontier for innovation, competition, and productivity, Tech. rep..McKinsey Global Institute, New York2011
- A logical calculus of the ideas immanent in nervous activity.Bull Math Biophys. 1943; 5: 115-133
- The perceptron: A probabilistic model for information storage and organization in the brain.Psychol Rev. 1958; 65: 386-408
- Learning representations by back-propagating errors.Nature. 1986; 323: 533-536
Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database. IEEE Conference on computer vision and Pattern Recognition, IEEE. Miami (FL), June 20, 2009.
- Imagenet classification with deep convolutional neural networks.in: Pereira F. Burges C.J.C. Bottou L. Advances in neural information processing systems. Curran Associates, Inc, Denver (CO)2012: 1097-1105
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Fort Lauderdale (FL); 2011. p. 315–23.
Maas AL, Hannun AY, Ng AY. Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th Annual International Conference on Machine Learning, Vol. 30. Atlanta (GA); 2013. p. 3.
Rifai S, Vincent P, Muller X, et al. Contractive autoencoders: explicit invariance during feature extraction. Proceedings of the 28th International Conference on International Conference on Machine Learning. Bellevue (WA): ACM; 2011. p. 833–840.
Rifai S, Mesnil G, Vincent P, et al. Higher order contractive auto-encoder. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Athens (Greece): Springer; 2011. p. 645–60.
Vincent P, Larochelle H, Bengio Y, et al. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning. Helsinki (Finland): ACM; 2008. p. 1096– 1103.
Kingma DP, Welling M. Auto-encoding variational bayes, arXiv preprint arXiv:1312.6114.
- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.J Mach Learn Res. 2010; 11: 3371-3408
- Generative adversarial nets.Adv Neural Inf Process Syst. 2014; 27: 2672-2680
- Adversarial similarity network for evaluating image alignment in deep learning based registration.in: Frangi A. Schnabel J. Davatzikos C. Medical image computing and computer Assisted Intervention – MICCAI 2018. Springer International Publishing, Cham (Switzerland)2018: 739-746
Mahapatra D, Antony B, Sedai S, et al. Deformable medical image registration using generative adversarial networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, DC: IEEE; 2018. p. 1449–1453.
Kazeminia S, Baur C, Kuijper A, et al. GANs for medical image analysis, arXiv preprint arXiv:1809.06222.
- Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs).in: Frangi A. Schnabel J. Davatzikos C. Molecular imaging, reconstruction and analysis of Moving body organs, and stroke imaging and treatment. Springer International Publishing, Cham (Switzerland)2017: 43-51
Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: A review. Med Image Anal 2019. https://doi.org/10.1016/j.media.2019.101552.
- Medical image synthesis with deep convolutional adversarial networks.IEEE Trans Biomed Eng. 2018; 65: 2720-2730
- GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification.Neurocomputing. 2018; 321: 321-331
- Medical image synthesis with context-aware generative adversarial networks.in: Frangi A. Schnabel J. Davatzikos C. Medical image computing and computer Assisted Intervention - MICCAI 2017. Springer International Publishing, Cham (Switzerland)2017: 417-425
- 3D fetal skull reconstruction from 2DUS via deep conditional generative networks.in: Frangi A. Schnabel J. Davatzikos C. Medical image computing and computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018: 383-391
Biffi C, Cerrolaza JJ, Tarroni G, et al. 3d high-resolution cardiac segmentation reconstruction from 2d views using conditional variational autoencoders, arXiv preprint arXiv:1902.11000.
- Long short-term memory.Neural Comput. 1997; 9: 1735-1780
Cho K, Van Merrïenboer B, Bahdanau D, et al. On the properties of neural machine translation: Encoder-decoder approaches, arXiv preprint arXiv:1409.1259.
Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. NIPS 2014 Workshop on Deep Learning. Montreal (QC), December 8, 2014.
- On the practical computational power of finite precision rnns for language recognition. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).Association for Computational Linguistics, Melbourne (Australia)2018: 740-745
- Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.PMLR, Sardinia (Italy)2010: 249-256
- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision.IEEE, Las Condes (Chile)2015: 1026-1034
- Dropout: a simple way to prevent neural networks from overfitting.J Mach Learn Res. 2014; 15: 1929-1958
- A survey on image data augmentation for deep learning.J Big Data. 2019; 6: 60
- Data augmentation for braintumor segmentation: A review.Front Comput Neurosci. 2019; 13: 83
- A survey of predictive modeling on imbalanced domains.ACM Comput Surv. 2016; 49: 1-31
- Transfer learning.Cambridge University Press, Cambridge2020
- Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.Eur Radiol. 2019; 30: 413-424
Tousignant A, Lemaˆıtre P, Precup D, et al. Prediction of disease progression in multiple sclerosis patients using deep learning analysis of MRI data. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, Vol. 102. London (UK): MIDL; 2019. p. 483–92.
- A deep learning-based radiomics model for differentiating benign and malignant renal tumors.Translational Oncol. 2019; 12: 292-300
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas (NV): IEEE; 2016. p. 770–8.
- Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE, Las Vegas (NV)2016: 2818-2826
Lin T.-Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision. Venice (Italy): IEEE; 2017. p. 2980–8.
Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City (UT): IEEE; 2018. p. 4510–20.
He K, Zhang X, Ren S, et al. Identity mappings in deep residual networks. European Conference on Computer Vision. Amsterdam (The Netherlands): Springer; 2016. p. 630–45.
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Munich (Germany): Springer; 2015. p. 234–41.
Hashemi SR, Prabhu SP, Warfield SK, et al. Exclusive independent probability estimation using deep 3d fully convolutional densenets for isointense infant brain MRI segmentation, arXiv preprint arXiv:1809.08168.
- Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of alzheimer’s disease.Int J Comput Assist Radiol Surg. 2020; 15: 445-455
Milletari F, Navab N, Ahmadi S.-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth International Conference on 3D Vision (3DV), IEEE, Stanford University, California, October 25, 2016. p. 565–71.
- Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.Med Image Anal. 2020; 60: 101602
Simonyan K, Zisserman A. Very deep convolutional networks for largescale image recognition, arXiv preprint arXiv: 1409.1556.
Chen L, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, arXiv preprint. arXiv:1606.00915.
Chen L, Papandreou G, Kokkinos I, et al. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, arXiv preprint arXiv:1606.00915.
- A deep learning framework for unsupervised affine and deformable image registration.Med Image Anal. 2019; 52: 128-143
Balakrishnan G, Zhao A, Sabuncu MR, et al. Voxelmorph: A learning framework for deformable medical image registration, arXiv preprint arXiv:1809.05231.
Shen Z, Han X, Xu Z, et al. Networks for joint affine and nonparametric image registration, arXiv preprint arXiv:1903.08811.
Eppenhof KAJ, Lafarge MW, Pluim JPW. Progressively growing convolutional networks for end-to-end deformable image registration. In: Angelini ED, Landman BA, editors. Medical imaging 2019: image processing, ociety of Photo-Optical Instrumentation Engineers (SPIE). Bellingham (WA): SPIE Press; 2019. p. 338–44.
Yang H, Sun J, Carass A, et al. Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN, arXiv preprint arXiv:1809.04536.
Zhu J.-Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision. Venice (Italy): IEEE; 2017. p. 2223–2232.
- CT-To-MR conditional generative adversarial networks for ischemic stroke lesion segmentation.IEEE Int Conf Healthc Inform. 2019; : 1-7
Bass C, Dai T, Billot B, et al. Image synthesis with a convolutional capsule generative adversarial network. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, Vol. 102 of Proceedings of Machine Learning Research. London (UK): PMLR; 2019. p. 39–62.
LaLonde R, Bagci U, Capsules for object segmentation, arXiv preprint arXiv:1804.04241.
Sabour S, Frosst N, Hinton GE, Dynamic routing between capsules, arXiv preprint arXiv:1710.09829.
Sriram A, Zbontar J, Murrell T, et al, GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction, arXiv preprint arXiv:1910.12325.
- Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.Eur Radiol. 2019; 29: 6163-6171
- Medical image denoising using convolutional denoising autoencoders. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).IEEE, Barcelona (Spain)2016: 241-246
- Medical image denoising using convolutional neural network: a residual learning approach.J Supercomput. 2019; 75: 704-718
Article info
Publication history
Footnotes
Preprint submitted to Neuroimaging Clinics February 27, 2020.
Funding Information: Dr Samuel Kadoury is a Canada Research Chair (CRC) in Medical Imaging and Assisted Intervention.
