Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015. ,
DOI : 10.1109/CVPR.2015.7298965
Very deep convolutional networks for large-scale image recognition, 1409. ,
Extra materials for this paper, 2017. ,
Neonatal brain image segmentation in longitudinal MRI studies, NeuroImage, vol.49, issue.1, pp.391-400, 2010. ,
DOI : 10.1016/j.neuroimage.2009.07.066
URL : http://europepmc.org/articles/pmc2764995?pdf=render
AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI, NeuroImage, vol.65, pp.97-108, 2013. ,
DOI : 10.1016/j.neuroimage.2012.08.009
Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates, Intl. Work. on Machine Learning in Medical Imaging, pp.27-35, 2016. ,
DOI : 10.1109/TPAMI.2012.143
Morphology-driven automatic segmentation of MR images of the neonatal brain, Medical Image Analysis, vol.16, issue.8, pp.1565-1579, 2012. ,
DOI : 10.1016/j.media.2012.07.006
Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images, Proc. of SPIE Medical Imaging, p.941315, 2015. ,
LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images, NeuroImage, vol.108, pp.160-172, 2015. ,
DOI : 10.1016/j.neuroimage.2014.12.042
A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.93-96, 2016. ,
DOI : 10.1109/EMBC.2016.7590648
Gradient-based learning applied to document recognition, Proc. of the IEEE, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation, NeuroImage, vol.108, pp.214-224, 2015. ,
DOI : 10.1016/j.neuroimage.2014.12.061
URL : http://europepmc.org/articles/pmc4323729?pdf=render
Deep learning for medical image segmentation, 2015. ,
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network, IEEE Transactions on Medical Imaging, vol.35, issue.5, pp.1252-1261, 2016. ,
DOI : 10.1109/TMI.2016.2548501
Fully convolutional networks for multi-modality isointense infant brain image segmentation, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp.1342-1345, 2016. ,
DOI : 10.1109/ISBI.2016.7493515
URL : http://europepmc.org/articles/pmc5031138?pdf=render
VoxResNet: Deep voxelwise residual networks for volumetric brain segmentation, 2016. ,
DOI : 10.1016/j.neuroimage.2017.04.041
Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016. ,
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385
Neonatal brain MRI segmentation: A review, Computers in Biology and Medicine, vol.64, pp.163-178, 2015. ,
DOI : 10.1016/j.compbiomed.2015.06.016
MRI segmentation of the human brain: Challenges, methods, and applications, Computational and Mathematical Methods in Medicine, vol.2015, 2015. ,
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique, IEEE Transactions on Medical Imaging, vol.35, issue.5, pp.1153-1158, 2016. ,
DOI : 10.1109/TMI.2016.2553401
Deep Neural Networks for Fast Segmentation of 3D Medical Images, Proc. of MICCAI, pp.158-165, 2016. ,
DOI : 10.1109/ICCV.2015.123
Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network, Proc. of MICCAI, pp.246-253, 2013. ,
DOI : 10.1007/978-3-642-40763-5_31
A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations, Proc. of MICCAI, pp.520-527, 2014. ,
DOI : 10.1007/978-3-319-10404-1_65
URL : https://hal.archives-ouvertes.fr/hal-01669719
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning, IEEE Transactions on Medical Imaging, vol.35, issue.5, pp.1285-1298, 2016. ,
DOI : 10.1109/TMI.2016.2528162
URL : http://arxiv.org/pdf/1602.03409
Chest pathology detection using deep learning with non-medical training, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp.294-297, 2015. ,
DOI : 10.1109/ISBI.2015.7163871
DeCAF: A deep convolutional activation feature for generic visual recognition, ICML, pp.647-655, 2014. ,
Deep Retinal Image Understanding, Proc. of MICCAI, pp.140-148, 2016. ,
DOI : 10.1007/978-3-319-24888-2_17
URL : http://arxiv.org/pdf/1609.01103
Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge, Medical Image Analysis, vol.20, issue.1, pp.135-151, 2015. ,
DOI : 10.1016/j.media.2014.11.001
MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans, Computational Intelligence and Neuroscience, vol.20, issue.1, pp.10-1155, 2015. ,
DOI : 10.1016/j.neuroimage.2014.12.042
Comparing images using the Hausdorff distance, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.9, pp.850-863, 1993. ,
DOI : 10.1109/34.232073