Automated medical image segmentation techniques, Journal of Medical Physics, vol.35, issue.1, pp.3-14, 2010. ,
Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique, IEEE Transactions on Medical Imaging, vol.35, pp.1153-1159, 2016. ,
Speech recognition with deep recurrent neural networks, CoRR, 2013. ,
Long-term recurrent convolutional networks for visual recognition and description, CoRR, 2014. ,
Cma-es for hyperparameter optimization of deep neural networks, 2016. ,
Random search for hyper-parameter optimization, J. Mach. Learn. Res, vol.13, pp.281-305, 2012. ,
Firecaffe: near-linear acceleration of deep neural network training on compute clusters, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2592-2600, 2016. ,
Horovod: fast and easy distributed deep learning in tensorflow, CoRR, 2018. ,
Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, pp.888-905, 2000. ,
Google's neural machine translation system: Bridging the gap between human and machine translation, 2016. ,
Building high-level features using large scale unsupervised learning, Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML'12, pp.507-514, 2012. ,
Large scale distributed deep networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, vol.1, pp.1223-1231, 2012. ,
Demystifying parallel and distributed deep learning: An in-depth concurrency analysis, CoRR, 2018. ,
Heterogeneity-aware distributed parameter servers, Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, pp.463-478, 2017. ,
Accurate, large minibatch SGD: training imagenet in 1 hour, CoRR, 2017. ,
Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012. ,
Don't decay the learning rate, increase the batch size, CoRR, 2017. ,
Gradient-based learning applied to document recognition, Intelligent Signal Processing, pp.306-351, 2001. ,
Adam: A method for stochastic optimization, CoRR, 2014. ,
Deep learning with cots hpc systems, International conference on machine learning, pp.1337-1345, 2013. ,
Heterogeneity-aware distributed parameter servers, Proceedings of the 2017 ACM International Conference on Management of Data, pp.463-478, 2017. ,
Scaling distributed machine learning with the parameter server, Proceedings of the 11th USENIX Conference on Operating Systems Design and Implementation, OSDI'14, pp.583-598, 2014. ,
Project adam: Building an efficient and scalable deep learning training system, 11th {USENIX} Symposium on Operating Systems Design and Implementation, pp.571-582, 2014. ,
Scalable distributed dnn training using commodity gpu cloud computing, INTERSPEECH, 2015. ,
Putting the pieces together, addison-westley, p.5, 2001. ,
Component-based frameworks for e-commerce, Communications of the ACM, vol.43, issue.10, pp.61-61, 2000. ,
Application of component-based software engineering in building a surveillance robot, Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), pp.651-658, 2014. ,
Using service-oriented architecture and component-based development to build web service applications, Rational Software Corporation, vol.6, pp.1-16, 2002. ,
A component based services architecture for building distributed applications, Proceedings the Ninth International Symposium on High-Performance Distributed Computing, pp.51-59, 2000. ,
Comdes-ii: A component-based framework for generative development of distributed real-time control systems, 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007), pp.199-208, 2007. ,
Jaskel: A java skeleton-based framework for structured cluster and grid computing, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06), vol.1, p.4, 2006. ,
Pipedream: Fast and efficient pipeline parallel dnn training, 2018. ,
Staleness-aware async-sgd for distributed deep learning, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16, pp.2350-2356, 2016. ,
Bandwidth optimal all-reduce algorithms for clusters of workstations, Journal of Parallel and Distributed Computing, vol.69, issue.2, pp.117-124, 2009. ,
Communication efficient distributed machine learning with the parameter server, Advances in Neural Information Processing Systems, pp.19-27, 2014. ,
Adding virtualization capabilities to the Grid'5000 testbed, Cloud Computing and Services Science, vol.367, pp.3-20, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00946971
Infiniband scalability in open mpi, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium, p.10, 2006. ,
Open mpi: Goals, concept, and design of a next generation mpi implementation, European Parallel Virtual Machine/Message Passing Interface Users' Group Meeting, pp.97-104, 2004. ,
U-net: Convolutional networks for biomedical image segmentation, CoRR, 2015. ,
Fully convolutional networks for semantic segmentation, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. ,
Multiscale cnns for brain tumor segmentation and diagnosis, Comp. Math. Methods in Medicine, vol.2016, issue.7, pp.1-8356294, 2016. ,
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Transactions on Medical Imaging, vol.34, pp.1993-2024, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00935640
Review of mri-based brain tumor image segmentation using deep learning methods, Procedia Comput. Sci, vol.102, pp.317-324, 2016. ,
Benchmark for algorithms segmenting the left atrium from 3d ct and mri datasets, IEEE transactions on medical imaging, vol.34, issue.7, pp.1460-1473, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01260607