Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med Image Anal, vol.12, issue.1, pp.26-41, 2008. ,
Semi-supervised learning for network-based cardiac MR image segmentation, vol.MICCAI, pp.253-260, 2017. ,
VoxelMorph: a learning framework for deformable medical image registration, 2018. ,
Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved, IEEE Trans Med Imaging, vol.37, issue.11, pp.2514-2525, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01803621
High dimensional data clustering, Computational Statistics and Data Analysis, vol.52, pp.502-519, 2007. ,
URL : https://hal.archives-ouvertes.fr/inria-00548573
Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the american heart association, 2002. ,
A radiomics approach to computer-aided diagnosis with cardiac cine-MRI, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017. ,
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis, 2018. ,
Shaping the future through innovations: from medical imaging to precision medicine, Med Image Anal, vol.33, pp.19-26, 2016. ,
Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, vol.283, pp.381-390, 2017. ,
Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance, Phys Med Biol, vol.61, pp.8640-8663, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01434871
4D modelling for rapid assessment of biventricular function in congenital heart disease, The International Journal of Cardiovascular Imaging, vol.34, issue.3, pp.407-417, 2017. ,
Semisupervised learning for biomedical image segmentation via forest oriented super pixels(voxels). MICCAI, pp.702-710, 2017. ,
Enhancing labeldriven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking, Bildverarbeitung für die Medizin, pp.309-314, 2019. ,
Comparing algorithms for diffeomorphic registration: stationary LDDMM and diffeomorphic demons, Proc. MFCA, pp.24-35, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00629883
What do we need to build explainable AI systems for the medical domain, 2017. ,
Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017. ,
Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017. ,
Fully convolutional multiscale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers, 2018. ,
Learning a probabilistic model for diffeomorphic registration, IEEE Transactions on Medical Imaging, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01978339
Unsupervised probabilistic deformation modeling for robust diffeomorphic registration, Proc. Deep Learning in Medical Image Analysis (DLMIA), MICCAI'18 Workshop, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01845688
Non-rigid image registration using fully convolutional networks with deep self-supervision, 2017. ,
LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm, NeuroImage, vol.81, pp.470-483, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00819895
Learning-based regularization for cardiac strain analysis with ability for domain adaptation, 2018. ,
Class-specific variable selection in high-dimensional discriminant analysis through bayesian sparsity, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01811514
Flow network based cardiac motion tracking leveraging learned feature matching, pp.279-286, 2017. ,
Scikitlearn: machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Understanding the "demon's algorithm": 3D non-rigid registration by gradient descent. MICCAI'99, pp.597-605, 1999. ,
UK Biobank's cardiovascular magnetic resonance protocol, J Cardiovasc Magn Reson, vol.18, issue.8, p.8, 2016. ,
Joint learning of motion estimation and segmentation for cardiac MR image sequences, pp.472-480, 2018. ,
Joint motion estimation and segmentation from undersampled cardiac MR image, pp.55-63, 2018. ,
Evaluation framework for algorithms segmenting short axis cardiac MRI, The MIDAS Journal -Cardiac MR Left Ventricle Segmentation Challenge, 2009. ,
U-net: Convolutional networks for biomedical image segmentation, MICCAI, vol.9351, pp.234-241, 2015. ,
Learning clinically useful information from images: past, present and future, Medical Image Analysis, vol.33, pp.13-18, 2016. ,
Highly accurate fast lung CT registration, Image Processing, vol.8669, 2013. ,
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge, IEEE J Biomed Health Inform, vol.22, issue.2, pp.503-515, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01533805
Cardiac image modelling: breadth and depth in heart disease, Medical Image Analysis, vol.33, pp.38-43, 2016. ,
DOI : 10.1016/j.media.2016.06.027
URL : http://europepmc.org/articles/pmc5123588?pdf=render
Fast marginal likelihood maximisation for sparse bayesian models, 2003. ,
End-toend unsupervised deformable image registration with a convolutional neural network. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, Conjunction with MICCAI 2017, pp.204-212, 2017. ,
Four challenges in medical image analysis from an industrial perspective, Medical Image Analysis, vol.33, pp.44-49, 2016. ,
DOI : 10.1016/j.media.2016.06.023
Automatic segmentation and disease classification using cardiac cine MR images, Proc. Statistical Atlases and Computational Models of the Heart (STACOM), ACDC challenge, MICCAI'17 Workshop, 2017. ,
DOI : 10.1007/978-3-319-75541-0_11
URL : http://arxiv.org/pdf/1708.01141
Full left ventricle quantification via deep multitask relationships learning, Med Image Anal, vol.43, pp.54-65, 2018. ,
DOI : 10.1016/j.media.2017.09.005
Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion, pp.613-621, 2018. ,
3D motion modeling and reconstruction of left ventricle wall in cardiac MRI, ternational Conference on Functional Imaging and Modeling of the Heart, pp.481-492, 2017. ,
3D consistent and robust segmentation of cardiac images by deep learning with spatial propagation, IEEE Trans Med Imaging, vol.37, issue.9, pp.2137-2148, 2018. ,