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Publications of year 2020

Books and proceedings

  1. Nicholas Ayache, Alain Damasio, Yuval Noah Harari, Cathy O'Neil, and Nicolas Revel. Nouvelle enquête sur l'intelligence artificielle, Champs Actuel. Flammarion, June 2020.
    @book{ayache:hal-03111667,
    TITLE = {{Nouvelle enqu{\^e}te sur l'intelligence artificielle}},
    AUTHOR = {Ayache, Nicholas and Damasio, Alain and Harari, Yuval Noah and O'Neil, Cathy and Revel, Nicolas},
    url-hal= {https://hal.inria.fr/hal-03111667},
    PUBLISHER = {{Flammarion}},
    SERIES = {Champs Actuel},
    YEAR = {2020},
    MONTH = Jun,
    HAL_ID = {hal-03111667},
    HAL_VERSION = {v1},
    
    }
    


  2. Xavier Pennec, Stephan Sommer, and Tom Fletcher. Riemannian Geometric Statistics in Medical Image Analysis. Elsevier, 2020.
    @book{pennec:hal-02341896,
    TITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
    AUTHOR = {Pennec, Xavier and Sommer, Stephan and Fletcher, Tom},
    url-hal= {https://hal.inria.fr/hal-02341896},
    PUBLISHER = {{Elsevier}},
    YEAR = {2020},
    DOI = {10.1016/C2017-0-01561-6},
    HAL_ID = {hal-02341896},
    HAL_VERSION = {v1},
    
    }
    


Thesis

  1. Julian Krebs. Robust Medical Image Registration and Motion Modeling based on Machine Learning. Theses, Université Côte d'Azur, France ; INRIA Sophia Antipolis, France, June 2020. Keyword(s): Medical imaging, Image registration, motion modeling, artificial intelligence, machine learning, variational autoencoder, sudden cardiac death, imagerie médicale, recalage d'images, modélisation du mouvement, intelligence artificielle, apprentissage profond, autoencodeur variationnel, mort cardiaque subite.
    @phdthesis{krebs:tel-02954033,
    TITLE = {{Robust Medical Image Registration and Motion Modeling based on Machine Learning}},
    AUTHOR = {Krebs, Julian},
    url-hal= {https://hal.archives-ouvertes.fr/tel-02954033},
    SCHOOL = {{Universit{\'e} C{\^o}te d'Azur, France ; INRIA Sophia Antipolis, France}},
    YEAR = {2020},
    MONTH = Jun,
    KEYWORDS = {Medical imaging ; Image registration ; motion modeling ; artificial intelligence ; machine learning ; variational autoencoder ; sudden cardiac death ; imagerie m{\'e}dicale ; recalage d'images ; mod{\'e}lisation du mouvement ; intelligence artificielle ; apprentissage profond ; autoencodeur variationnel ; mort cardiaque subite},
    TYPE = {Theses},
    PDF = {https://hal.archives-ouvertes.fr/tel-02954033/file/PhD_thesis_Krebs.pdf},
    HAL_ID = {tel-02954033},
    HAL_VERSION = {v1},
    
    }
    


  2. Wen Wei. Learning Brain Alterations in Multiple Sclerosis from Multimodal Neuroimaging Data. Theses, Université Côte d'Azur ; INRIA Sophia Antipolis - Méditerranée, June 2020. Keyword(s): Multiple Sclerosis, PET Imaging, MR Imaging, Brain Alterations, Deep Learning, Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Image Synthesis, Missing MRI Sequences, Missing Modalities, Sclérose en Plaques, IRM, TEP, Apprentissage en Profondeur, Altérations Cérébrales, Modalités Manquantes, Séquences IRM Manquantes, Réseau de Neurones Convolutifs (CNN), Réseaux Antagonistes Génératifs (GAN), Synthèse d'images.
    @phdthesis{wei:tel-02862395,
    TITLE = {{Learning Brain Alterations in Multiple Sclerosis from Multimodal Neuroimaging Data}},
    AUTHOR = {Wei, Wen},
    url-hal= {https://hal.inria.fr/tel-02862395},
    SCHOOL = {{Universit{\'e} C{\^o}te d'Azur ; INRIA Sophia Antipolis - M{\'e}diterran{\'e}e}},
    YEAR = {2020},
    MONTH = Jun,
    KEYWORDS = {Multiple Sclerosis ; PET Imaging ; MR Imaging ; Brain Alterations ; Deep Learning ; Convolutional Neural Networks (CNN) ; Generative Adversarial Network (GAN) ; Image Synthesis ; Missing MRI Sequences ; Missing Modalities ; Scl{\'e}rose en Plaques ; IRM ; TEP ; Apprentissage en Profondeur ; Alt{\'e}rations C{\'e}r{\'e}brales ; Modalit{\'e}s Manquantes ; S{\'e}quences IRM Manquantes ; R{\'e}seau de Neurones Convolutifs (CNN) ; R{\'e}seaux Antagonistes G{\'e}n{\'e}ratifs (GAN) ; Synth{\`e}se d'images},
    TYPE = {Theses},
    PDF = {https://hal.inria.fr/tel-02862395/file/thesis-wen-2020-06-09_fr.pdf},
    HAL_ID = {tel-02862395},
    HAL_VERSION = {v1},
    
    }
    


Articles in journal, book chapters

  1. Benoît Audelan and Hervé Delingette. Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model. Medical Image Analysis, 68:101895, November 2020. Keyword(s): Bayesian learning, Image segmentation, Unsupervised quality control, Bayesian learning.
    @article{audelan:hal-03044140,
    TITLE = {{Unsupervised quality control of segmentations based on a smoothness and intensity probabilistic model}},
    AUTHOR = {Audelan, Beno{\^i}t and Delingette, Herv{\'e}},
    url-hal= {https://hal.inria.fr/hal-03044140},
    JOURNAL = {{Medical Image Analysis}},
    PUBLISHER = {{Elsevier}},
    VOLUME = {68},
    PAGES = {101895},
    YEAR = {2020},
    MONTH = Nov,
    DOI = {10.1016/j.media.2020.101895},
    KEYWORDS = {Bayesian learning ; Image segmentation ; Unsupervised quality control ; Bayesian learning},
    PDF = {https://hal.inria.fr/hal-03044140/file/revised_manuscript.pdf},
    HAL_ID = {hal-03044140},
    HAL_VERSION = {v1},
    
    }
    


  2. Paul Blanc-Durand, Simon Jégou, Salim Kanoun, Alina Berriolo-Riedinger, Caroline M Bodet-Milin, Françoise Kraeber-Bodéré, Thomas Carlier, Steven Le Gouill, René-Olivier Casasnovas, Michel Meignan, and Emmanuel Itti. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. European Journal of Nuclear Medicine and Molecular Imaging, pp 9, 2020. Keyword(s): U-net, Total metabolic tumour volume, Segmentation, Positron emission tomography, Lymphoma, Convolutional neural network, Deep learning.
    @article{blancdurand:inserm-03006852,
    TITLE = {{Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network}},
    AUTHOR = {Blanc-Durand, Paul and J{\'e}gou, Simon and Kanoun, Salim and Berriolo-Riedinger, Alina and Bodet-Milin, Caroline M and Kraeber-Bod{\'e}r{\'e}, Fran{\c c}oise and Carlier, Thomas and Le Gouill, Steven and Casasnovas, Ren{\'e}-Olivier and Meignan, Michel and Itti, Emmanuel},
    url-hal= {https://www.hal.inserm.fr/inserm-03006852},
    JOURNAL = {{European Journal of Nuclear Medicine and Molecular Imaging}},
    PUBLISHER = {{Springer Verlag (Germany)}},
    PAGES = {9},
    YEAR = {2020},
    DOI = {10.1007/s00259-020-05080-7},
    KEYWORDS = {U-net ; Total metabolic tumour volume ; Segmentation ; Positron emission tomography ; Lymphoma ; Convolutional neural network ; Deep learning},
    PDF = {https://www.hal.inserm.fr/inserm-03006852/file/Blanc-Durand2020Eq13.pdf},
    HAL_ID = {inserm-03006852},
    HAL_VERSION = {v1},
    
    }
    


  3. Emmanuel Chevallier and Nicolas Guigui. A Bi-Invariant Statistical Model Parametrized by Mean and Covariance on Rigid Motions. Entropy, 22(4):432, 2020. Keyword(s): differential of the exponential, Euclidean groups, rigid motions, wrapped distributions, sampling, density estimation, moment-matching estimator.
    @article{chevallier:hal-02568245,
    TITLE = {{A Bi-Invariant Statistical Model Parametrized by Mean and Covariance on Rigid Motions}},
    AUTHOR = {Chevallier, Emmanuel and Guigui, Nicolas},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02568245},
    JOURNAL = {{Entropy}},
    PUBLISHER = {{MDPI}},
    VOLUME = {22},
    NUMBER = {4},
    PAGES = {432},
    YEAR = {2020},
    DOI = {10.3390/e22040432},
    KEYWORDS = {differential of the exponential ; Euclidean groups ; rigid motions ; wrapped distributions ; sampling ; density estimation ; moment-matching estimator},
    PDF = {https://hal.archives-ouvertes.fr/hal-02568245/file/SEnHAL_Chevallier_Guigui.pdf},
    HAL_ID = {hal-02568245},
    HAL_VERSION = {v1},
    
    }
    


  4. James S Duncan, Michel F Insana, and Nicholas Ayache. Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning. Proceedings of the IEEE, 14:3-10, January 2020.
    @article{duncan:hal-02395429,
    TITLE = {{Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning}},
    AUTHOR = {Duncan, James S and Insana, Michel F and Ayache, Nicholas},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02395429},
    JOURNAL = {{Proceedings of the IEEE}},
    PUBLISHER = {{Institute of Electrical and Electronics Engineers}},
    VOLUME = {14},
    PAGES = {3-10},
    YEAR = {2020},
    MONTH = Jan,
    PDF = {https://hal.archives-ouvertes.fr/hal-02395429v2/file/Proceedings_of_IEEE_Editors_Overview_Article-10.pdf},
    HAL_ID = {hal-02395429},
    HAL_VERSION = {v2},
    
    }
    


  5. Simon Heeke, Jonathan Benzaquen, Véronique Hofman, Elodie Long-Mira, Virginie Lespinet, Olivier Bordone, Charles-Hugo Marquette, Hervé Delingette, Marius Ilié, and Paul Hofman. Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability. Journal of Thoracic Oncology, 15(9):1535-1540, September 2020.
    @article{heeke:hal-02972033,
    TITLE = {{Comparison of Three Sequencing Panels Used for the Assessment of Tumor Mutational Burden in NSCLC Reveals Low Comparability}},
    AUTHOR = {Heeke, Simon and Benzaquen, Jonathan and Hofman, V{\'e}ronique and Long-Mira, Elodie and Lespinet, Virginie and Bordone, Olivier and Marquette, Charles-Hugo and Delingette, Herv{\'e} and Ili{\'e}, Marius and Hofman, Paul},
    url-hal= {https://hal.inria.fr/hal-02972033},
    JOURNAL = {{Journal of Thoracic Oncology}},
    PUBLISHER = {{Lippincott, Williams \& Wilkins}},
    VOLUME = {15},
    NUMBER = {9},
    PAGES = {1535-1540},
    YEAR = {2020},
    MONTH = Sep,
    DOI = {10.1016/j.jtho.2020.05.013},
    HAL_ID = {hal-02972033},
    HAL_VERSION = {v1},
    
    }
    


  6. Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec. Geomstats: A Python Package for Riemannian Geometry in Machine Learning. Journal of Machine Learning Research, 21(223):1-9, December 2020. Keyword(s): differential geometry, Riemannian geometry, statistics, machine learning, manifold.
    @article{miolane:hal-02536154,
    TITLE = {{Geomstats: A Python Package for Riemannian Geometry in Machine Learning}},
    AUTHOR = {Miolane, Nina and Guigui, Nicolas and Le Brigant, Alice and Mathe, Johan and Hou, Benjamin and Thanwerdas, Yann and Heyder, Stefan and Peltre, Olivier and Koep, Niklas and Zaatiti, Hadi and Hajri, Hatem and Cabanes, Yann and Gerald, Thomas and Chauchat, Paul and Shewmake, Christian and Brooks, Daniel and Kainz, Bernhard and Donnat, Claire and Holmes, Susan and Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02536154},
    JOURNAL = {{Journal of Machine Learning Research}},
    HAL_LOCAL_REFERENCE = {explo},
    PUBLISHER = {{Microtome Publishing}},
    VOLUME = {21},
    NUMBER = {223},
    PAGES = {1-9},
    YEAR = {2020},
    MONTH = Dec,
    KEYWORDS = {differential geometry ; Riemannian geometry ; statistics ; machine learning ; manifold},
    PDF = {https://hal.inria.fr/hal-02536154v2/file/19-027.pdf},
    HAL_ID = {hal-02536154},
    HAL_VERSION = {v2},
    
    }
    


  7. Pawel Mlynarski, Hervé Delingette, Hamza A Alghamdi, Pierre-Yves Bondiau, and Nicholas Ayache. Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy. Journal of Medical Imaging, 7(1):1-21, February 2020. Keyword(s): segmentation, organs at risk, Convolutional Neural Networks, radiotherapy, MRI.
    @article{mlynarski:hal-02181181,
    TITLE = {{Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy}},
    AUTHOR = {Mlynarski, Pawel and Delingette, Herv{\'e} and Alghamdi, Hamza A and Bondiau, Pierre-Yves and Ayache, Nicholas},
    url-hal= {https://hal.inria.fr/hal-02181181},
    JOURNAL = {{Journal of Medical Imaging}},
    PUBLISHER = {{SPIE Digital Library}},
    VOLUME = {7},
    NUMBER = {1},
    PAGES = {1--21},
    YEAR = {2020},
    MONTH = Feb,
    DOI = {10.1117/1.JMI.7.1.014502},
    KEYWORDS = {segmentation ; organs at risk ; Convolutional Neural Networks ; radiotherapy ; MRI},
    PDF = {https://hal.inria.fr/hal-02181181v2/file/article.pdf},
    HAL_ID = {hal-02181181},
    HAL_VERSION = {v2},
    
    }
    


  8. Pamela Moceri, Nicolas Duchateau, Stephane Gillon, Lolita Jaunay, Delphine Baudouy, Fabien Squara, Emile Ferrari, and Maxime Sermesant. 3D right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading. European Heart Journal - Cardiovascular Imaging, 2020.
    @article{moceri:hal-02913107,
    TITLE = {{3D right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading}},
    AUTHOR = {Moceri, Pamela and Duchateau, Nicolas and Gillon, Stephane and Jaunay, Lolita and Baudouy, Delphine and Squara, Fabien and Ferrari, Emile and Sermesant, Maxime},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02913107},
    JOURNAL = {{European Heart Journal - Cardiovascular Imaging}},
    PUBLISHER = {{Oxford UP}},
    YEAR = {2020},
    PDF = {https://hal.archives-ouvertes.fr/hal-02913107/file/Moceri_EHJCI_2020.pdf},
    HAL_ID = {hal-02913107},
    HAL_VERSION = {v1},
    
    }
    


  9. Fanny Orlhac, Augustin Lecler, Julien Savatovski, Jessica Goya-Outi, Christophe Nioche, Frédérique Charbonneau, Nicholas Ayache, Frédérique Frouin, Loïc Duron, and Irène Buvat. How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. European Radiology, 2020.
    @article{orlhac:hal-02945627,
    TITLE = {{How can we combat multicenter variability in MR radiomics? Validation of a correction procedure}},
    AUTHOR = {Orlhac, Fanny and Lecler, Augustin and Savatovski, Julien and Goya-Outi, Jessica and Nioche, Christophe and Charbonneau, Fr{\'e}d{\'e}rique and Ayache, Nicholas and Frouin, Fr{\'e}d{\'e}rique and Duron, Lo{\"i}c and Buvat, Ir{\`e}ne},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02945627},
    JOURNAL = {{European Radiology}},
    PUBLISHER = {{Springer Verlag}},
    YEAR = {2020},
    PDF = {https://hal.archives-ouvertes.fr/hal-02945627/file/Manuscrit_HAL.pdf},
    HAL_ID = {hal-02945627},
    HAL_VERSION = {v1},
    
    }
    


  10. Raphaël Sivera, Nicolas Capet, Valeria Manera, Roxane Fabre, Marco Lorenzi, Hervé Delingette, Xavier Pennec, Nicholas Ayache, and Philippe Robert. Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort. Neurobiology of Aging, 94:50-59, October 2020. Keyword(s): Multidomain intervention, Clinical trial, Subjective memory complaint, Deformation-based morphometry.
    @article{sivera:hal-02166357,
    TITLE = {{Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort}},
    AUTHOR = {Sivera, Rapha{\"e}l and Capet, Nicolas and Manera, Valeria and Fabre, Roxane and Lorenzi, Marco and Delingette, Herv{\'e} and Pennec, Xavier and Ayache, Nicholas and Robert, Philippe},
    url-hal= {https://hal.inria.fr/hal-02166357},
    JOURNAL = {{Neurobiology of Aging}},
    PUBLISHER = {{Elsevier}},
    VOLUME = {94},
    PAGES = {50-59},
    YEAR = {2020},
    MONTH = Oct,
    DOI = {10.1016/j.neurobiolaging.2019.11.020},
    KEYWORDS = {Multidomain intervention ; Clinical trial ; Subjective memory complaint ; Deformation-based morphometry},
    PDF = {https://hal.inria.fr/hal-02166357v2/file/paper-mapt-final.pdf},
    HAL_ID = {hal-02166357},
    HAL_VERSION = {v2},
    
    }
    


  11. Wen Wei, Emilie Poirion, Benedetta Bodini, Matteo Tonietto, Stanley Durrleman, Olivier Colliot, Bruno Stankoff, and Nicholas Ayache. Predicting PET-derived Myelin Content from Multisequence MRI for Individual Longitudinal Analysis in Multiple Sclerosis. NeuroImage, 223C(117308), December 2020. Keyword(s): Conditional GANs, Attention Mechanism, PET Imaging, Multisequence MRI, Demyelination and Remyelination, Deep Learning, Multiple Sclerosis.
    @article{wei:hal-02922534,
    TITLE = {{Predicting PET-derived Myelin Content from Multisequence MRI for Individual Longitudinal Analysis in Multiple Sclerosis}},
    AUTHOR = {Wei, Wen and Poirion, Emilie and Bodini, Benedetta and Tonietto, Matteo and Durrleman, Stanley and Colliot, Olivier and Stankoff, Bruno and Ayache, Nicholas},
    url-hal= {https://hal.inria.fr/hal-02922534},
    JOURNAL = {{NeuroImage}},
    PUBLISHER = {{Elsevier}},
    VOLUME = {223C},
    NUMBER = {117308},
    YEAR = {2020},
    MONTH = Dec,
    DOI = {10.1016/j.neuroimage.2020.117308},
    KEYWORDS = {Conditional GANs ; Attention Mechanism ; PET Imaging ; Multisequence MRI ; Demyelination and Remyelination ; Deep Learning ; Multiple Sclerosis},
    PDF = {https://hal.inria.fr/hal-02922534/file/elsarticle-template.pdf},
    HAL_ID = {hal-02922534},
    HAL_VERSION = {v1},
    
    }
    


  12. Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen E Petersen, and Nicholas Ayache. Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs. Frontiers in Cardiovascular Medicine, 7:164, November 2020. Keyword(s): Cardiac pathology, Cardiac motion, Cluster analysis, UK Biobank, Feature extraction, Gaussian mixture model, Cine MRI.
    @article{zheng:hal-02043380,
    TITLE = {{Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs}},
    AUTHOR = {Zheng, Qiao and Delingette, Herv{\'e} and Fung, Kenneth and Petersen, Steffen E and Ayache, Nicholas},
    url-hal= {https://hal.inria.fr/hal-02043380},
    JOURNAL = {{Frontiers in Cardiovascular Medicine}},
    PUBLISHER = {{Frontiers Media}},
    VOLUME = {7},
    PAGES = {164},
    YEAR = {2020},
    MONTH = Nov,
    DOI = {10.3389/fcvm.2020.539788},
    KEYWORDS = {Cardiac pathology ; Cardiac motion ; Cluster analysis ; UK Biobank ; Feature extraction ; Gaussian mixture model ; Cine MRI},
    PDF = {https://hal.inria.fr/hal-02043380/file/1902.05811.pdf},
    HAL_ID = {hal-02043380},
    HAL_VERSION = {v1},
    
    }
    


  13. Nicholas Ayache. Medical Imaging in the Age of Artificial Intelligence. In Nordlinger B., Villani C., and Rus D., editors, Healthcare and Artificial Intelligence, pages 89-91. Springer International Publishing, March 2020.
    @incollection{ayache:hal-02522507,
    TITLE = {{Medical Imaging in the Age of Artificial Intelligence}},
    AUTHOR = {Ayache, Nicholas},
    url-hal= {https://hal.inria.fr/hal-02522507},
    BOOKTITLE = {{Healthcare and Artificial Intelligence}},
    EDITOR = {Nordlinger B. and Villani C. and Rus D.},
    PUBLISHER = {{Springer International Publishing}},
    PAGES = {89-91},
    YEAR = {2020},
    MONTH = Mar,
    DOI = {10.1007/978-3-030-32161-1\_13},
    PDF = {https://hal.inria.fr/hal-02522507/file/Ayache-Nordlinger2020-AuthorVersion.pdf},
    HAL_ID = {hal-02522507},
    HAL_VERSION = {v1},
    
    }
    


  14. Nina Miolane, Loïc Devilliers, and Xavier Pennec. Bias on estimation in quotient space and correction methods: Applications to statistics on organ shapes. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 9, pages 343-376. Elsevier, 2020.
    @incollection{miolane:hal-02342155,
    TITLE = {{Bias on estimation in quotient space and correction methods: Applications to statistics on organ shapes}},
    AUTHOR = {Miolane, Nina and Devilliers, Lo{\"i}c and Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02342155},
    BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
    PUBLISHER = {{Elsevier}},
    NUMBER = {Chap. 9},
    PAGES = {343-376},
    YEAR = {2020},
    DOI = {10.1016/B978-0-12-814725-2.00017-0},
    PDF = {https://hal.inria.fr/hal-02342155/file/chapterQuotient.pdf},
    HAL_ID = {hal-02342155},
    HAL_VERSION = {v1},
    
    }
    


  15. Xavier Pennec. Advances in Geometric Statistics for Manifold Dimension Reduction. In Handbook of Variational Methods for Nonlinear Geometric Data, pages 339-359. Springer International Publishing, April 2020.
    @incollection{pennec:hal-02560938,
    TITLE = {{Advances in Geometric Statistics for Manifold Dimension Reduction}},
    AUTHOR = {Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02560938},
    BOOKTITLE = {{Handbook of Variational Methods for Nonlinear Geometric Data}},
    PUBLISHER = {{Springer International Publishing}},
    PAGES = {339-359},
    YEAR = {2020},
    MONTH = Apr,
    DOI = {10.1007/978-3-030-31351-7\_11},
    PDF = {https://hal.inria.fr/hal-02560938/file/XP_GeomStats_ManifoldReductionPCA.pdf},
    HAL_ID = {hal-02560938},
    HAL_VERSION = {v1},
    
    }
    


  16. Xavier Pennec. Manifold-valued image processing with SPD matrices. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 3, pages 75-134. Elsevier, 2020.
    @incollection{pennec:hal-02341958,
    TITLE = {{Manifold-valued image processing with SPD matrices}},
    AUTHOR = {Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02341958},
    BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
    PUBLISHER = {{Elsevier}},
    NUMBER = {Chap. 3},
    PAGES = {75-134},
    YEAR = {2020},
    DOI = {10.1016/B978-0-12-814725-2.00010-8},
    PDF = {https://hal.inria.fr/hal-02341958/file/chapter3.pdf},
    HAL_ID = {hal-02341958},
    HAL_VERSION = {v1},
    
    }
    


  17. Xavier Pennec. Statistical analysis of organs' shapes and deformations: the Riemannian and the affine settings in computational anatomy. In Jean-François Uhl, Joaquim Jorge, Daniel Simoes Lopes, and Pedro F Campos, editors, Digital Anatomy - Applications of Virtual, Mixed and Augmented Reality, Human--Computer Interaction Series. Springer Nature, December 2020.
    @incollection{pennec:hal-02925156,
    TITLE = {{Statistical analysis of organs' shapes and deformations: the Riemannian and the affine settings in computational anatomy}},
    AUTHOR = {Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02925156},
    BOOKTITLE = {{Digital Anatomy - Applications of Virtual, Mixed and Augmented Reality}},
    EDITOR = {Jean-Fran{\c c}ois Uhl and Joaquim Jorge and Daniel Simoes Lopes and Pedro F Campos},
    PUBLISHER = {{Springer Nature}},
    SERIES = {Human--Computer Interaction Series},
    YEAR = {2020},
    MONTH = Dec,
    PDF = {https://hal.inria.fr/hal-02925156/file/2020_CompAnat_Pennec.pdf},
    HAL_ID = {hal-02925156},
    HAL_VERSION = {v1},
    
    }
    


  18. Xavier Pennec and Marco Lorenzi. Beyond Riemannian geometry: The affine connection setting for transformation groups. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 5, pages 169-229. Elsevier, 2020.
    @incollection{pennec:hal-02342137,
    TITLE = {{Beyond Riemannian geometry: The affine connection setting for transformation groups}},
    AUTHOR = {Pennec, Xavier and Lorenzi, Marco},
    url-hal= {https://hal.inria.fr/hal-02342137},
    BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
    PUBLISHER = {{Elsevier}},
    NUMBER = {Chap. 5},
    PAGES = {169-229},
    YEAR = {2020},
    DOI = {10.1016/B978-0-12-814725-2.00012-1},
    PDF = {https://hal.inria.fr/hal-02342137/file/chapter5.pdf},
    HAL_ID = {hal-02342137},
    HAL_VERSION = {v1},
    
    }
    


  19. Stefan Sommer, Tom Fletcher, and Xavier Pennec. Introduction to differential and Riemannian geometry. In Riemannian Geometric Statistics in Medical Image Analysis, number Chap. 1, pages 3-37. Elsevier, 2020.
    @incollection{sommer:hal-02341901,
    TITLE = {{Introduction to differential and Riemannian geometry}},
    AUTHOR = {Sommer, Stefan and Fletcher, Tom and Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02341901},
    BOOKTITLE = {{Riemannian Geometric Statistics in Medical Image Analysis}},
    PUBLISHER = {{Elsevier}},
    NUMBER = {Chap. 1},
    PAGES = {3-37},
    YEAR = {2020},
    DOI = {10.1016/b978-0-12-814725-2.00008-x},
    PDF = {https://hal.inria.fr/hal-02341901/file/chapter1.pdf},
    HAL_ID = {hal-02341901},
    HAL_VERSION = {v1},
    
    }
    


Conference articles

  1. Benoît Audelan, Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, and Hervé Delingette. Robust Fusion of Probability Maps. In MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Lima/ Virtuel, Peru, October 2020.
    @inproceedings{audelan:hal-02934590,
    TITLE = {{Robust Fusion of Probability Maps}},
    AUTHOR = {Audelan, Beno{\^i}t and Hamzaoui, Dimitri and Montagne, Sarah and Renard-Penna, Rapha{\"e}le and Delingette, Herv{\'e}},
    url-hal= {https://hal.inria.fr/hal-02934590},
    BOOKTITLE = {{MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention}},
    ADDRESS = {Lima/ Virtuel, Peru},
    YEAR = {2020},
    MONTH = Oct,
    PDF = {https://hal.inria.fr/hal-02934590/file/paper2268.pdf},
    HAL_ID = {hal-02934590},
    HAL_VERSION = {v1},
    
    }
    


  2. Jaume Banus, Maxime Sermesant, Oscar Camara, and Marco Lorenzi. Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets. In MICCAI 2020 - 23th International Conference on Medical Image Computing and Computer Assisted Intervention, Lima / Virtual, Peru, pages 478-486, October 2020. Keyword(s): Gaussian Process, Variational Inference, Lumped model, Missing features, Biomechanical simulation.
    @inproceedings{banus:hal-02952576,
    TITLE = {{Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets}},
    AUTHOR = {Banus, Jaume and Sermesant, Maxime and Camara, Oscar and Lorenzi, Marco},
    url-hal= {https://hal.inria.fr/hal-02952576},
    BOOKTITLE = {{MICCAI 2020 - 23th International Conference on Medical Image Computing and Computer Assisted Intervention}},
    ADDRESS = {Lima / Virtual, Peru},
    PAGES = {478-486},
    YEAR = {2020},
    MONTH = Oct,
    DOI = {10.1007/978-3-030-59725-2\_46},
    KEYWORDS = {Gaussian Process ; Variational Inference ; Lumped model ; Missing features ; Biomechanical simulation},
    PDF = {https://hal.inria.fr/hal-02952576v2/file/MICCAI_2020_Jaume%2823%29.pdf},
    HAL_ID = {hal-02952576},
    HAL_VERSION = {v2},
    
    }
    


  3. Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Daniel Brooks, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Bernhard Kainz, Claire Donnat, Susan Holmes, and Xavier Pennec. Introduction to Geometric Learning in Python with Geomstats. In Meghann Agarwal, Chris Calloway, Dillon Niederhut, and David Shupe, editors, SciPy 2020 - 19th Python in Science Conference, Austin, Texas, United States, pages 48-57, July 2020. Keyword(s): Index Terms-differential geometry, statistics, manifold, machine learning.
    @inproceedings{miolane:hal-02908006,
    TITLE = {{Introduction to Geometric Learning in Python with Geomstats}},
    AUTHOR = {Miolane, Nina and Guigui, Nicolas and Zaatiti, Hadi and Shewmake, Christian and Hajri, Hatem and Brooks, Daniel and Le Brigant, Alice and Mathe, Johan and Hou, Benjamin and Thanwerdas, Yann and Heyder, Stefan and Peltre, Olivier and Koep, Niklas and Cabanes, Yann and Gerald, Thomas and Chauchat, Paul and Kainz, Bernhard and Donnat, Claire and Holmes, Susan and Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02908006},
    BOOKTITLE = {{SciPy 2020 - 19th Python in Science Conference}},
    ADDRESS = {Austin, Texas, United States},
    EDITOR = {Meghann Agarwal and Chris Calloway and Dillon Niederhut and David Shupe},
    PAGES = {48-57},
    YEAR = {2020},
    MONTH = Jul,
    DOI = {10.25080/Majora-342d178e-007},
    KEYWORDS = {Index Terms-differential geometry ; statistics ; manifold ; machine learning},
    PDF = {https://hal.inria.fr/hal-02908006/file/geomstats.pdf},
    HAL_ID = {hal-02908006},
    HAL_VERSION = {v1},
    
    }
    


  4. Pamela Moceri, Nicolas Duchateau, N Dursent, Xavier Iriart, Sébastien Hascoet, D Baudouy, Emile Ferrari, and Maxime Sermesant. Right ventricular remodelling in CHD-PAH patients using 3D speckle tracking. In JESFC 2020 - 30es Journées Européennes de la Société Française de Cardiologie, volume 12 of Archives of Cardiovascular Diseases Supplements, Paris, France, pages 163-4, January 2020.
    @inproceedings{moceri:hal-02445303,
    TITLE = {{Right ventricular remodelling in CHD-PAH patients using 3D speckle tracking}},
    AUTHOR = {Moceri, Pamela and Duchateau, Nicolas and Dursent, N and Iriart, Xavier and Hascoet, S{\'e}bastien and Baudouy, D and Ferrari, Emile and Sermesant, Maxime},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02445303},
    BOOKTITLE = {{JESFC 2020 - 30es Journ{\'e}es Europ{\'e}ennes de la Soci{\'e}t{\'e} Fran{\c c}aise de Cardiologie}},
    ADDRESS = {Paris, France},
    SERIES = {Archives of Cardiovascular Diseases Supplements},
    VOLUME = {12},
    PAGES = {163-4},
    YEAR = {2020},
    MONTH = Jan,
    HAL_ID = {hal-02445303},
    HAL_VERSION = {v1},
    
    }
    


  5. Marta Nuñez-Garcia, Nicolas Cedilnik, Shuman Jia, Hubert Cochet, Marco Lorenzi, and Maxime Sermesant. Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data. In STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, Lima, Peru, October 2020. Keyword(s): Post-infarct cardiac remodeling, Ventricular arrhythmia, Cross-sectional data, Disease progression modeling.
    @inproceedings{nunezgarcia:hal-02961506,
    TITLE = {{Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data}},
    AUTHOR = {Nu{\~n}ez-Garcia, Marta and Cedilnik, Nicolas and Jia, Shuman and Cochet, Hubert and Lorenzi, Marco and Sermesant, Maxime},
    url-hal= {https://hal.inria.fr/hal-02961506},
    BOOKTITLE = {{STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart}},
    ADDRESS = {Lima, Peru},
    YEAR = {2020},
    MONTH = Oct,
    KEYWORDS = {Post-infarct cardiac remodeling ; Ventricular arrhythmia ; Cross-sectional data ; Disease progression modeling},
    PDF = {https://hal.inria.fr/hal-02961506v2/file/scar_maturation_stacom2020_cr.pdf},
    HAL_ID = {hal-02961506},
    HAL_VERSION = {v2},
    
    }
    


  6. Marta Nuñez-Garcia, Nicolas Cedilnik, Shuman Jia, Maxime Sermesant, and Hubert Cochet. Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view. In STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, Lima, Peru, October 2020. Keyword(s): Cardiac imaging, Short-axis view, Deep learning segmentation, Automatic image reformatting.
    @inproceedings{nunezgarcia:hal-02961500,
    TITLE = {{Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view}},
    AUTHOR = {Nu{\~n}ez-Garcia, Marta and Cedilnik, Nicolas and Jia, Shuman and Sermesant, Maxime and Cochet, Hubert},
    url-hal= {https://hal.inria.fr/hal-02961500},
    BOOKTITLE = {{STACOM 2020 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart}},
    ADDRESS = {Lima, Peru},
    YEAR = {2020},
    MONTH = Oct,
    KEYWORDS = {Cardiac imaging ; Short-axis view ; Deep learning segmentation ; Automatic image reformatting},
    PDF = {https://hal.inria.fr/hal-02961500/file/SAX_reformat_Stacom2020_cr.pdf},
    HAL_ID = {hal-02961500},
    HAL_VERSION = {v1},
    
    }
    


  7. Fanny Orlhac, Thibaut Cassou-Mounat, Jean-Yves Pierga, Marie Luporsi, Christophe Nioche, Charles Bouveyron, Nicholas Ayache, Nina Jehanno, Alain Livartowski, and Irene Buvat. Can we identify ''twin patients'' to predict response to neoadjuvant chemotherapy in breast cancer?. In SNMMI Annual Meeting, Virtual Meeting, United States, July 2020.
    @inproceedings{orlhac:inserm-02952453,
    TITLE = {{Can we identify ''twin patients'' to predict response to neoadjuvant chemotherapy in breast cancer?}},
    AUTHOR = {Orlhac, Fanny and Cassou-Mounat, Thibaut and Pierga, Jean-Yves and Luporsi, Marie and Nioche, Christophe and Bouveyron, Charles and Ayache, Nicholas and Jehanno, Nina and Livartowski, Alain and Buvat, Irene},
    url-hal= {https://www.hal.inserm.fr/inserm-02952453},
    BOOKTITLE = {{SNMMI Annual Meeting}},
    ADDRESS = {Virtual Meeting, United States},
    YEAR = {2020},
    MONTH = Jul,
    PDF = {https://www.hal.inserm.fr/inserm-02952453/file/Abstract_twin_HAL.pdf},
    HAL_ID = {inserm-02952453},
    HAL_VERSION = {v1},
    
    }
    


  8. Fanny Orlhac, Anne-Capucine Rollet, Irène Buvat, Jacques Darcourt, Véronique Bourg, Christophe Nioche, Charles Bouveyron, Nicholas Ayache, and Olivier Humbert. Identifying a reliable radiomic signature from scarce data: illustration for 18F-FDOPA PET images in glioblastoma patients. In EANM Annual Meeting - Annual Meeting of the European Association of Nuclear Medicine, Virtual Meeting, Austria, October 2020.
    @inproceedings{orlhac:inserm-02952445,
    TITLE = {{Identifying a reliable radiomic signature from scarce data: illustration for 18F-FDOPA PET images in glioblastoma patients}},
    AUTHOR = {Orlhac, Fanny and Rollet, Anne-Capucine and Buvat, Ir{\`e}ne and Darcourt, Jacques and Bourg, V{\'e}ronique and Nioche, Christophe and Bouveyron, Charles and Ayache, Nicholas and Humbert, Olivier},
    url-hal= {https://www.hal.inserm.fr/inserm-02952445},
    BOOKTITLE = {{EANM Annual Meeting - Annual Meeting of the European Association of Nuclear Medicine}},
    ADDRESS = {Virtual Meeting, Austria},
    YEAR = {2020},
    MONTH = Oct,
    PDF = {https://www.hal.inserm.fr/inserm-02952445/file/Absrract_Gliomes_HAL.pdf},
    HAL_ID = {inserm-02952445},
    HAL_VERSION = {v1},
    
    }
    


  9. Santiago Silva, Andre Altmann, Boris Gutman, and Marco Lorenzi. Fed-BioMed: A general open-source frontendframework for federated learning in healthcare. In MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning, DCL: MICCAI Workshop on Distributed and Collaborative Learning, Lima/ Virtuel, Peru, pages 201-210, October 2020. Springer. Keyword(s): federated learning, healthcare, medical imaging.
    @inproceedings{silva:hal-02966789,
    TITLE = {{Fed-BioMed: A general open-source frontendframework for federated learning in healthcare}},
    AUTHOR = {Silva, Santiago and Altmann, Andre and Gutman, Boris and Lorenzi, Marco},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02966789},
    BOOKTITLE = {{MICCAI 2020 - 23rd International Conference on Medical Image Computing and Computer Assisted Intervention - 1st Workshop on Distributed and Collaborative Learning}},
    ADDRESS = {Lima/ Virtuel, Peru},
    PUBLISHER = {{Springer}},
    SERIES = {DCL: MICCAI Workshop on Distributed and Collaborative Learning},
    PAGES = {201-210},
    YEAR = {2020},
    MONTH = Oct,
    KEYWORDS = {federated learning ; healthcare ; medical imaging},
    PDF = {https://hal.archives-ouvertes.fr/hal-02966789/file/DCL_MICCAI_2020_Paper-1%281%29.pdf},
    HAL_ID = {hal-02966789},
    HAL_VERSION = {v1},
    
    }
    


  10. Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, and Hervé Delingette. A Deep Learning based Fast Signed Distance Map Generation. In MIDL 2020 - Medical Imaging with Deep Learning, Montréal, Canada, July 2020.
    @inproceedings{wang:hal-02570026,
    TITLE = {{A Deep Learning based Fast Signed Distance Map Generation}},
    AUTHOR = {Wang, Zihao and Vandersteen, Clair and Demarcy, Thomas and Gnansia, Dan and Raffaelli, Charles and Guevara, Nicolas and Delingette, Herv{\'e}},
    url-hal= {https://hal.inria.fr/hal-02570026},
    BOOKTITLE = {{MIDL 2020 - Medical Imaging with Deep Learning}},
    ADDRESS = {Montr{\'e}al, Canada},
    YEAR = {2020},
    MONTH = Jul,
    PDF = {https://hal.inria.fr/hal-02570026/file/MIDL2020__A_Deep_Learning_based_Fast_Signed_Distance_MapGeneration%20%281%29.pdf},
    HAL_ID = {hal-02570026},
    HAL_VERSION = {v1},
    
    }
    


Patents, standards

  1. Julian Krebs and Tommaso Mansi. Method and System for Deep Motion Model Learning in Medical Images. US20200090345A1, United States, March 2020.
    @patent{krebs:hal-02536459,
    TITLE = {{Method and System for Deep Motion Model Learning in Medical Images}},
    AUTHOR = {Krebs, Julian and Mansi, Tommaso},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02536459},
    NUMBER = {US20200090345A1},
    ADDRESS = {United States},
    YEAR = {2020},
    MONTH = Mar,
    HAL_ID = {hal-02536459},
    HAL_VERSION = {v1},
    
    }
    


  2. Julian Krebs, Tommaso Mansi, Hervé Delingette, and Nicholas Ayache. Probabilist Motion Model for Generating Medical Images or Medical Image Sequences. US16834269, United States, October 2020.
    @patent{krebs:hal-02977569,
    TITLE = {{Probabilist Motion Model for Generating Medical Images or Medical Image Sequences}},
    AUTHOR = {Krebs, Julian and Mansi, Tommaso and Delingette, Herv{\'e} and Ayache, Nicholas},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02977569},
    NUMBER = {US16834269},
    ADDRESS = {United States},
    YEAR = {2020},
    MONTH = Oct,
    HAL_ID = {hal-02977569},
    HAL_VERSION = {v1},
    
    }
    


Miscellaneous

  1. Clément Abi Nader, Nicholas Ayache, Giovanni B Frisoni, Philippe Robert, and Marco Lorenzi. Simulating the outcome of amyloid treatments in Alzheimer's Disease from multi-modal imaging and clinical data. Note: Working paper or preprint, November 2020.
    @unpublished{abinader:hal-02968724,
    TITLE = {{Simulating the outcome of amyloid treatments in Alzheimer's Disease from multi-modal imaging and clinical data}},
    AUTHOR = {Abi Nader, Cl{\'e}ment and Ayache, Nicholas and Frisoni, Giovanni B and Robert, Philippe and Lorenzi, Marco},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02968724},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Nov,
    PDF = {https://hal.archives-ouvertes.fr/hal-02968724v3/file/preprint.pdf},
    HAL_ID = {hal-02968724},
    HAL_VERSION = {v3},
    
    }
    


  2. Nicolas Guigui and Xavier Pennec. Numerical Accuracy of Ladder Schemes for Parallel Transport on Manifolds. Note: Working paper or preprint, July 2020. Keyword(s): Parallel Transport, Riemannian Geometry, Numerical Methods.
    @unpublished{guigui:hal-02894783,
    TITLE = {{Numerical Accuracy of Ladder Schemes for Parallel Transport on Manifolds}},
    AUTHOR = {Guigui, Nicolas and Pennec, Xavier},
    url-hal= {https://hal.inria.fr/hal-02894783},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Jul,
    KEYWORDS = {Parallel Transport ; Riemannian Geometry ; Numerical Methods},
    PDF = {https://hal.inria.fr/hal-02894783/file/main.pdf},
    HAL_ID = {hal-02894783},
    HAL_VERSION = {v1},
    
    }
    


  3. Philipp Harms, Peter W. Michor, Xavier Pennec, and Stefan Sommer. Geometry of Sample Spaces. Note: 29 pages, 1 figure, October 2020.
    @unpublished{harms:hal-02972385,
    TITLE = {{Geometry of Sample Spaces}},
    AUTHOR = {Harms, Philipp and Michor, Peter W. and Pennec, Xavier and Sommer, Stefan},
    url-hal= {https://hal.inria.fr/hal-02972385},
    NOTE = {29 pages, 1 figure},
    YEAR = {2020},
    MONTH = Oct,
    HAL_ID = {hal-02972385},
    HAL_VERSION = {v1},
    
    }
    


  4. Alice Le Brigant, Nicolas Guigui, Sana Rebbah, and Stéphane Puechmorel. Classifying histograms of medical data using information geometry of beta distributions. Note: Working paper or preprint, June 2020. Keyword(s): histogram analysis, clustering, medical imaging, Information geometry, classification.
    @unpublished{lebrigant:hal-02671122,
    TITLE = {{Classifying histograms of medical data using information geometry of beta distributions}},
    AUTHOR = {Le Brigant, Alice and Guigui, Nicolas and Rebbah, Sana and Puechmorel, St{\'e}phane},
    url-hal= {https://hal.archives-ouvertes.fr/hal-02671122},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Jun,
    KEYWORDS = {histogram analysis ; clustering ; medical imaging ; Information geometry ; classification},
    PDF = {https://hal.archives-ouvertes.fr/hal-02671122/file/info_geom_beta.pdf},
    HAL_ID = {hal-02671122},
    HAL_VERSION = {v1},
    
    }
    


  5. Zihao Wang and Hervé Delingette. Quasi-Symplectic Langevin Variational Au-Toencoder. Note: Working paper or preprint, November 2020. Keyword(s): 41A05, 41A10, 65D05, 65D17 Automatic landmark detec-.
    @unpublished{wang:hal-03024748,
    TITLE = {{Quasi-Symplectic Langevin Variational Au-Toencoder}},
    AUTHOR = {Wang, Zihao and Delingette, Herv{\'e}},
    url-hal= {https://hal.inria.fr/hal-03024748},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Nov,
    KEYWORDS = {41A05 ; 41A10 ; 65D05 ; 65D17 Automatic landmark detec-},
    PDF = {https://hal.inria.fr/hal-03024748/file/ICLR_Langevin_VAE.pdf},
    HAL_ID = {hal-03024748},
    HAL_VERSION = {v1},
    
    }
    


  6. Zihao Wang, Zhifei Xu, Jiayi He, Hervé Delingette, and Jun FAN. Long Short-Term Memory Neural Equalizer. Note: Working paper or preprint, November 2020. Keyword(s): decision feedback equalizer, neuromorphic computing, deep learning, LSTM.
    @unpublished{wang:hal-03022865,
    TITLE = {{Long Short-Term Memory Neural Equalizer}},
    AUTHOR = {Wang, Zihao and Xu, Zhifei and He, Jiayi and Delingette, Herv{\'e} and FAN, Jun},
    url-hal= {https://hal.inria.fr/hal-03022865},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Nov,
    KEYWORDS = {decision feedback equalizer ; neuromorphic computing ; deep learning ; LSTM},
    PDF = {https://hal.inria.fr/hal-03022865/file/Neuron_Equalizer__Copy_%20%282%29.pdf},
    HAL_ID = {hal-03022865},
    HAL_VERSION = {v1},
    
    }
    


  7. Wang Zihao, Vandersteen Clair, Raffaelli Charles, Guevara Nicolas, and Delingette Hervé. One-shot Learning Landmarks Detection. Note: Working paper or preprint, November 2020.
    @unpublished{zihao:hal-03024759,
    TITLE = {{One-shot Learning Landmarks Detection}},
    AUTHOR = {Zihao, Wang and Clair, Vandersteen and Charles, Raffaelli and Nicolas, Guevara and Herv{\'e}, Delingette},
    url-hal= {https://hal.inria.fr/hal-03024759},
    NOTE = {working paper or preprint},
    YEAR = {2020},
    MONTH = Nov,
    PDF = {https://hal.inria.fr/hal-03024759/file/Landmark_Detection.pdf},
    HAL_ID = {hal-03024759},
    HAL_VERSION = {v1},
    
    }
    



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