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Publications of year 2023

Thesis

  1. Gaëtan Desrues. Personalised 3D electromechanical models of the heart for cardiac resynchronisation therapy planning in heart failure patients. Theses, Université Côte d'Azur, March 2023. Keyword(s): Patient-specific simulation, Digital twin, ECG, CRT, Cardiac electrophysiology, Biomechanics, Finite element method, Artificial intelligence, Simulation personnalisée, Jumeau numérique, ECG, CRT, Électrophysiologie cardiaque, Biomécanique, Méthode des éléments finis, Intelligence artificielle. 
    @phdthesis{desrues:tel-04220830,
TITLE = {{Personalised 3D electromechanical models of the heart for cardiac resynchronisation therapy planning in heart failure patients}},
AUTHOR = {Desrues, Ga{\"e}tan},
url-hal= {https://theses.hal.science/tel-04220830},
NUMBER = {2023COAZ4030},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Mar,
KEYWORDS = {Patient-specific simulation ; Digital twin ; ECG ; CRT ; Cardiac electrophysiology ; Biomechanics ; Finite element method ; Artificial intelligence ; Simulation personnalis{\'e}e ; Jumeau num{\'e}rique ; ECG ; CRT ; {\'E}lectrophysiologie cardiaque ; Biom{\'e}canique ; M{\'e}thode des {\'e}l{\'e}ments finis ; Intelligence artificielle},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04220830v1/file/2023COAZ4030.pdf},
HAL_ID = {tel-04220830},
HAL_VERSION = {v1},

}


  2. Yann Fraboni. Reliability and robustness of federated learning in practical applications. Theses, Université Côte d'Azur, May 2023. Keyword(s): Federated learning, Heterogeneous data, Privacy, Distributed optimization, Bias, Apprentissage fédéré, Données hétérogènes, Protection des données, Optimisation distribuée, Biais. 
    @phdthesis{fraboni:tel-04141520,
TITLE = {{Reliability and robustness of federated learning in practical applications}},
AUTHOR = {Fraboni, Yann},
url-hal= {https://theses.hal.science/tel-04141520},
NUMBER = {2023COAZ4033},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = May,
KEYWORDS = {Federated learning ; Heterogeneous data ; Privacy ; Distributed optimization ; Bias ; Apprentissage f{\'e}d{\'e}r{\'e} ; Donn{\'e}es h{\'e}t{\'e}rog{\`e}nes ; Protection des donn{\'e}es ; Optimisation distribu{\'e}e ; Biais},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04141520v1/file/2023COAZ4033.pdf},
HAL_ID = {tel-04141520},
HAL_VERSION = {v1},

}


  3. Dimitri Hamzaoui. AI-based diagnosis of prostate cancer from multiparametric MRI. Theses, Université Côte d'Azur, June 2023. Keyword(s): Machine learning, Prostate-cancer, Inter-expert variability, Segmentation, Consensus, Medical imaging, Artificial intelligence, Apprentissage profond, Prostate-cancer, Variabilité inter-expert, Segmentation, Consensus, Imagerie médicale, Intelligence artificielle. 
    @phdthesis{hamzaoui:tel-04166332,
TITLE = {{AI-based diagnosis of prostate cancer from multiparametric MRI}},
AUTHOR = {Hamzaoui, Dimitri},
url-hal= {https://hal.science/tel-04166332},
NUMBER = {2023COAZ4044},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {Machine learning ; Prostate-cancer ; Inter-expert variability ; Segmentation ; Consensus ; Medical imaging ; Artificial intelligence ; Apprentissage profond ; Prostate-cancer ; Variabilit{\'e} inter-expert ; Segmentation ; Consensus ; Imagerie m{\'e}dicale ; Intelligence artificielle},
TYPE = {Theses},
PDF = {https://hal.science/tel-04166332v2/file/2023COAZ4044.pdf},
HAL_ID = {tel-04166332},
HAL_VERSION = {v2},

}


  4. Etrit Haxholli. Scalable and flexible density estimation for complex data distributions. Theses, Université Côte d'Azur, October 2023. Keyword(s): Density estimation, Normalizing flows, Diffusion models, Extreme value theorie, Tail modelling, Loss function distributions, Peaks-over-threshold, Cross-tail-estimation, Estimation de la densité, Flux de normalisation, Modèles de diffusion, Théorie des valeurs extrêmes, Modélisation de la queue, Distributions de la fonction de perte, Pics-au-dessus-du-seuil, Estimation de la queue croisée. 
    @phdthesis{haxholli:tel-04416188,
TITLE = {{Scalable and flexible density estimation for complex data distributions}},
AUTHOR = {Haxholli, Etrit},
url-hal= {https://theses.hal.science/tel-04416188},
NUMBER = {2023COAZ4083},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Oct,
KEYWORDS = {Density estimation ; Normalizing flows ; Diffusion models ; Extreme value theorie ; Tail modelling ; Loss function distributions ; Peaks-over-threshold ; Cross-tail-estimation ; Estimation de la densit{\'e} ; Flux de normalisation ; Mod{\`e}les de diffusion ; Th{\'e}orie des valeurs extr{\^e}mes ; Mod{\'e}lisation de la queue ; Distributions de la fonction de perte ; Pics-au-dessus-du-seuil ; Estimation de la queue crois{\'e}e},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04416188v2/file/2023COAZ4083.pdf},
HAL_ID = {tel-04416188},
HAL_VERSION = {v2},

}


  5. Victoriya Kashtanova. Learning cardiac electrophysiology dynamics with PDE-based physiological constraints for data-driven personalised predictions. Theses, Université Côte d'Azur, June 2023. Keyword(s): Physics-based learning, Deep learning, Cardiac electrophysiology, Model personalisation, PDE, Simulations, Apprentissage basé sur la physique, Apprentissage profond, Electrophysiologie cardiaque, Personnalisation de modèle, EDP, Simulations. 
    @phdthesis{kashtanova:tel-04176798,
TITLE = {{Learning cardiac electrophysiology dynamics with PDE-based physiological constraints for data-driven personalised predictions}},
AUTHOR = {Kashtanova, Victoriya},
url-hal= {https://hal.science/tel-04176798},
NUMBER = {2023COAZ4043},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {Physics-based learning ; Deep learning ; Cardiac electrophysiology ; Model personalisation ; PDE ; Simulations ; Apprentissage bas{\'e} sur la physique ; Apprentissage profond ; Electrophysiologie cardiaque ; Personnalisation de mod{\`e}le ; EDP ; Simulations},
TYPE = {Theses},
PDF = {https://hal.science/tel-04176798v2/file/2023COAZ4043.pdf},
HAL_ID = {tel-04176798},
HAL_VERSION = {v2},

}


  6. Elodie Maignant. Barycentric embeddings for geometric manifold learning : with application to shapes and graphs. Theses, Université Côte d'Azur, December 2023. Keyword(s): Geometric learning, Riemannian and barycentric geometry, Manifold learning, Quotient manifolds, Kendall shape spaces, Statistical graph analysis, Apprentissage géométrique, Géométrie riemannienne et barycentrique, Apprentissage de variétés, Variétés quotient, Espaces de formes de Kendall, Analyse statistique de graphes. 
    @phdthesis{maignant:tel-04452790,
TITLE = {{Barycentric embeddings for geometric manifold learning : with application to shapes and graphs}},
AUTHOR = {Maignant, Elodie},
url-hal= {https://theses.hal.science/tel-04452790},
NUMBER = {2023COAZ4096},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Dec,
KEYWORDS = {Geometric learning ; Riemannian and barycentric geometry ; Manifold learning ; Quotient manifolds ; Kendall shape spaces ; Statistical graph analysis ; Apprentissage g{\'e}om{\'e}trique ; G{\'e}om{\'e}trie riemannienne et barycentrique ; Apprentissage de vari{\'e}t{\'e}s ; Vari{\'e}t{\'e}s quotient ; Espaces de formes de Kendall ; Analyse statistique de graphes},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04452790v2/file/2023COAZ4096.pdf},
HAL_ID = {tel-04452790},
HAL_VERSION = {v2},

}


  7. Morten Akhoj Pedersen. Riemannian and sub-riemannian methods for dimension reduction. Theses, Université Côte d'Azur ; Kobenhavns universitet, November 2023. Keyword(s): Geometrisk statistik, Differentialgeometry, Riemannsk geometri, Sub-Riemannsk geometri, Matematisk statistik, Maskinlaering, Geometric statistics, Sub-riemannian geometry, Mathematical statistics, Differential geometry, Géometrie riemannienne, Machine learning, Statistiques géométriques, Géometrie sous-riemannienne, Statistique mathématique, Géométrie différentielle, Géometrie riemannienne, Apprentissage automatique. 
    @phdthesis{pedersen:tel-04391602,
TITLE = {{Riemannian and sub-riemannian methods for dimension reduction}},
AUTHOR = {Pedersen, Morten Akh{{\o}}j},
url-hal= {https://theses.hal.science/tel-04391602},
NUMBER = {2023COAZ4087},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur ; K{{\o}}benhavns universitet}},
YEAR = {2023},
MONTH = Nov,
KEYWORDS = {Geometrisk statistik ; Differentialgeometry ; Riemannsk geometri ; Sub-Riemannsk geometri ; Matematisk statistik ; Maskinl{\ae}ring ; Geometric statistics ; Sub-riemannian geometry ; Mathematical statistics ; Differential geometry ; G{\'e}ometrie riemannienne ; Machine learning ; Statistiques g{\'e}om{\'e}triques ; G{\'e}ometrie sous-riemannienne ; Statistique math{\'e}matique ; G{\'e}om{\'e}trie diff{\'e}rentielle ; G{\'e}ometrie riemannienne ; Apprentissage automatique},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04391602v2/file/2023COAZ4087.pdf},
HAL_ID = {tel-04391602},
HAL_VERSION = {v2},

}


  8. Santiago Smith Silva Rincon. Federated Learning of Biomedical Data in Multicentric Imaging Studies. Theses, UCA, Inria, July 2023. Keyword(s): federated learning, healthcare, data protection, GDPR, CCPA, medical imaging, data harmonization, meta-analysis, mega-analysis, Fed-BioMed, Bayesian optimization, random effect models, FedComBat, apprentissage fédéré, santé, protection des données, RGPD, CCPA, imagerie médicale, harmonisation des données, méta-analyse, méganalyse, Fed-BioMed, optimisation bayésienne, modèles à effets aléatoires, FedComBat. 
    @phdthesis{silvarincon:tel-04417044,
TITLE = {{Federated Learning of Biomedical Data in Multicentric Imaging Studies}},
AUTHOR = {Silva Rincon, Santiago Smith},
url-hal= {https://inria.hal.science/tel-04417044},
SCHOOL = {{UCA, Inria}},
YEAR = {2023},
MONTH = Jul,
KEYWORDS = {federated learning ; healthcare ; data protection ; GDPR ; CCPA ; medical imaging ; data harmonization ; meta-analysis ; mega-analysis ; Fed-BioMed ; Bayesian optimization ; random effect models ; FedComBat ; apprentissage f{\'e}d{\'e}r{\'e} ; sant{\'e} ; protection des donn{\'e}es ; RGPD ; CCPA ; imagerie m{\'e}dicale ; harmonisation des donn{\'e}es ; m{\'e}ta-analyse ; m{\'e}ganalyse ; Fed-BioMed ; optimisation bay{\'e}sienne ; mod{\`e}les {\`a} effets al{\'e}atoires ; FedComBat},
TYPE = {Theses},
PDF = {https://inria.hal.science/tel-04417044v2/file/PhD_Manuscript_Santiago_SILVA.pdf},
HAL_ID = {tel-04417044},
HAL_VERSION = {v2},

}


  9. Paul Tourniaire. AI-based selection of imaging and biological markers predictive of therapy response in lung cancer. Theses, Université Côte d'Azur, June 2023. Keyword(s): Digital pathology, Multiple instance learning, Mixed supervision, Deep learning, Survival analysis, Lung cancer, Immunotherapy, Histopathologie numérique, Apprentissage multi-instance, Supervision mélangée, Apprentissage profond, Analyse de survie, Cancer du poumon, Immunothérapie. 
    @phdthesis{tourniaire:tel-04189450,
TITLE = {{AI-based selection of imaging and biological markers predictive of therapy response in lung cancer}},
AUTHOR = {Tourniaire, Paul},
url-hal= {https://theses.hal.science/tel-04189450},
NUMBER = {2023COAZ4041},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {Digital pathology ; Multiple instance learning ; Mixed supervision ; Deep learning ; Survival analysis ; Lung cancer ; Immunotherapy ; Histopathologie num{\'e}rique ; Apprentissage multi-instance ; Supervision m{\'e}lang{\'e}e ; Apprentissage profond ; Analyse de survie ; Cancer du poumon ; Immunoth{\'e}rapie},
TYPE = {Theses},
PDF = {https://theses.hal.science/tel-04189450v2/file/2023COAZ4041.pdf},
HAL_ID = {tel-04189450},
HAL_VERSION = {v2},

}


  10. Yingyu Yang. Automatic analysis of cardiac function with artificial intelligence : multimodal approach for portable echocardiographic devices. Theses, Université Côte d'Azur, December 2023. Keyword(s): Cardiac function analysis, Deep learning, Echocardiography segmentation, Echocardiography motion tracking, Electrocardiogram decomposition, Multi-modal learning, Deep learning with uncertainty, Analyse de la fonction cardiaque, Apprentissage profond, Segmentation de l'échocardiographie, Suivi du mouvement en échocardiographie, Décomposition de l'électrocardiogramme, Apprentissage multimodal, Apprentissage profond avec incertitude. 
    @phdthesis{yang:tel-04422777,
TITLE = {{Automatic analysis of cardiac function with artificial intelligence : multimodal approach for portable echocardiographic devices}},
AUTHOR = {Yang, Yingyu},
url-hal= {https://inria.hal.science/tel-04422777},
NUMBER = {2023COAZ4107},
SCHOOL = {{Universit{\'e} C{\^o}te d'Azur}},
YEAR = {2023},
MONTH = Dec,
KEYWORDS = {Cardiac function analysis ; Deep learning ; Echocardiography segmentation ; Echocardiography motion tracking ; Electrocardiogram decomposition ; Multi-modal learning ; Deep learning with uncertainty ; Analyse de la fonction cardiaque ; Apprentissage profond ; Segmentation de l'{\'e}chocardiographie ; Suivi du mouvement en {\'e}chocardiographie ; D{\'e}composition de l'{\'e}lectrocardiogramme ; Apprentissage multimodal ; Apprentissage profond avec incertitude},
TYPE = {Theses},
PDF = {https://inria.hal.science/tel-04422777v2/file/2023COAZ4107.pdf},
HAL_ID = {tel-04422777},
HAL_VERSION = {v2},

}


Articles in journal, book chapters

  1. Arnaud Berenbaum, Hervé Delingette, Aurélien Maire, Cécile Poret, Claire Hassen-Khodja, Stéphane Bréant, Christel Daniel, Patricia Martel, Lamiae Grimaldi, Marie Frank, Emmanuel Durand, and Florent Besson. Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project. Applied Sciences, 13(9):5281, May 2023. Keyword(s): FDG PET, artificial intelligence, deep learning, convolutional neural network. 
    @article{berenbaum:hal-04081046,
TITLE = {{Performance of AI-Based Automated Classifications of Whole-Body FDG PET in Clinical Practice: The CLARITI Project}},
AUTHOR = {Berenbaum, Arnaud and Delingette, Herv{\'e} and Maire, Aur{\'e}lien and Poret, C{\'e}cile and Hassen-Khodja, Claire and Br{\'e}ant, St{\'e}phane and Daniel, Christel and Martel, Patricia and Grimaldi, Lamiae and Frank, Marie and Durand, Emmanuel and Besson, Florent},
url-hal= {https://inria.hal.science/hal-04081046},
JOURNAL = {{Applied Sciences}},
PUBLISHER = {{Multidisciplinary digital publishing institute (MDPI)}},
VOLUME = {13},
NUMBER = {9},
PAGES = {5281},
YEAR = {2023},
MONTH = May,
DOI = {10.3390/app13095281},
KEYWORDS = {FDG PET ; artificial intelligence ; deep learning ; convolutional neural network},
HAL_ID = {hal-04081046},
HAL_VERSION = {v1},

}


  2. Nina Brillat-Savarin, Carine Wu, Laurène Aupin, Camille Thoumin, Dimitri Hamzaoui, and Raphaële Renard-Penna. 3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score. European Journal of Radiology, 166:110974, September 2023. 
    @article{brillatsavarin:hal-04162794,
TITLE = {{3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score}},
AUTHOR = {Brillat-Savarin, Nina and Wu, Carine and Aupin, Laur{\`e}ne and Thoumin, Camille and Hamzaoui, Dimitri and Renard-Penna, Rapha{\"e}le},
url-hal= {https://hal.science/hal-04162794},
JOURNAL = {{European Journal of Radiology}},
PUBLISHER = {{Elsevier}},
VOLUME = {166},
PAGES = {110974},
YEAR = {2023},
MONTH = Sep,
DOI = {10.1016/j.ejrad.2023.110974},
HAL_ID = {hal-04162794},
HAL_VERSION = {v1},

}


  3. Nicolas Cedilnik, Mihaela Pop, Josselin Duchateau, Frédéric Sacher, Pierre Jaïs, Hubert Cochet, and Maxime Sermesant. Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall Thickness Heterogeneity. JACC: Clinical Electrophysiology, September 2023. Keyword(s): Cardiac modeling, Electrophysiology modeling, CT, Ventricular tachycardia, Medical simulation. 
    @article{cedilnik:hal-04255556,
TITLE = {{Efficient Patient-Specific Simulations of Ventricular Tachycardia Based on Computed Tomography-Defined Wall Thickness Heterogeneity}},
AUTHOR = {Cedilnik, Nicolas and Pop, Mihaela and Duchateau, Josselin and Sacher, Fr{\'e}d{\'e}ric and Ja{\"i}s, Pierre and Cochet, Hubert and Sermesant, Maxime},
url-hal= {https://inria.hal.science/hal-04255556},
JOURNAL = {{JACC: Clinical Electrophysiology}},
PUBLISHER = {{Elsevier}},
YEAR = {2023},
MONTH = Sep,
DOI = {10.1016/j.jacep.2023.08.008},
KEYWORDS = {Cardiac modeling ; Electrophysiology modeling ; CT ; Ventricular tachycardia ; Medical simulation},
PDF = {https://inria.hal.science/hal-04255556v1/file/cedilnik-jaccep-2023.pdf},
HAL_ID = {hal-04255556},
HAL_VERSION = {v1},

}


  4. Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, and Ron Kikinis. Comparison of physics-based deformable registration methods for image-guided neurosurgery. Frontiers in Digital Health, 5, December 2023. Keyword(s): Image-guided neurosurgery, physics-based deformable registration, finite element methods (FEM), high performance computing, mesh generation. 
    @article{chrisochoides:hal-04334718,
TITLE = {{Comparison of physics-based deformable registration methods for image-guided neurosurgery}},
AUTHOR = {Chrisochoides, Nikos and Liu, Yixun and Drakopoulos, Fotis and Kot, Andriy and Foteinos, Panos and Tsolakis, Christos and Billias, Emmanuel and Clatz, Olivier and Ayache, Nicholas and Fedorov, Andrey and Golby, Alex and Black, Peter and Kikinis, Ron},
url-hal= {https://inria.hal.science/hal-04334718},
JOURNAL = {{Frontiers in Digital Health}},
PUBLISHER = {{Frontiers Media S.A.}},
VOLUME = {5},
YEAR = {2023},
MONTH = Dec,
DOI = {10.3389/fdgth.2023.1283726},
KEYWORDS = {Image-guided neurosurgery ; physics-based deformable registration ; finite element methods (FEM) ; high performance computing ; mesh generation},
HAL_ID = {hal-04334718},
HAL_VERSION = {v1},

}


  5. Quentin Clairon, Chloé Pasin, Irene Balelli, Rodolphe Thiébaut, and Mélanie Prague. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach. Computational Statistics, September 2023. Keyword(s): Dynamic population models, Ordinary differential equations, Optimal control theory, Mechanistic models, Nonlinear mixed effects models, Clinical trial analysis. 
    @article{clairon:hal-03335826,
TITLE = {{Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach}},
AUTHOR = {Clairon, Quentin and Pasin, Chlo{\'e} and Balelli, Irene and Thi{\'e}baut, Rodolphe and Prague, M{\'e}lanie},
url-hal= {https://hal.science/hal-03335826},
JOURNAL = {{Computational Statistics}},
PUBLISHER = {{Springer Verlag}},
YEAR = {2023},
MONTH = Sep,
KEYWORDS = {Dynamic population models ; Ordinary differential equations ; Optimal control theory ; Mechanistic models ; Nonlinear mixed effects models ; Clinical trial analysis},
PDF = {https://hal.science/hal-03335826v2/file/Manuscript_Estimation_NLMEODE_via_OCA_CompStat%20%281%29.pdf},
HAL_ID = {hal-03335826},
HAL_VERSION = {v2},

}


  6. Francesco Cremonesi, Vincent Planat, Varvara Kalokyri, Haridimos Kondylakis, Tiziana Sanavia, Victor Miguel Mateos Resinas, Babita Singh, and Silvia Uribe. The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform. Journal of Biomedical Informatics, 141:104338, May 2023. Keyword(s): Federated learning, Data model, Healthcare, Omics, Lessons learned, Medical research, Graphical. 
    @article{cremonesi:hal-04600442,
TITLE = {{The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform}},
AUTHOR = {Cremonesi, Francesco and Planat, Vincent and Kalokyri, Varvara and Kondylakis, Haridimos and Sanavia, Tiziana and Miguel Mateos Resinas, Victor and Singh, Babita and Uribe, Silvia},
url-hal= {https://hal.science/hal-04600442},
JOURNAL = {{Journal of Biomedical Informatics}},
PUBLISHER = {{Elsevier}},
VOLUME = {141},
PAGES = {104338},
YEAR = {2023},
MONTH = May,
DOI = {10.1016/j.jbi.2023.104338},
KEYWORDS = {Federated learning ; Data model ; Healthcare ; Omics ; Lessons learned ; Medical research ; Graphical},
PDF = {https://hal.science/hal-04600442v1/file/20221020-hal-version-need-for-multimodal-health-data-modeling.pdf},
HAL_ID = {hal-04600442},
HAL_VERSION = {v1},

}


  7. Hind Dadoun, Hervé Delingette, Anne-Laure Rousseau, Eric de Kerviler, and Nicholas Ayache. Deep Clustering for Abdominal Organ Classification in US imaging. Journal of Medical Imaging, 10(3):034502, 2023. Keyword(s): ultrasound imaging, representation learning, deep clustering, semi-supervised learning. 
    @article{dadoun:hal-03773082,
TITLE = {{Deep Clustering for Abdominal Organ Classification in US imaging}},
AUTHOR = {Dadoun, Hind and Delingette, Herv{\'e} and Rousseau, Anne-Laure and de Kerviler, Eric and Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-03773082},
JOURNAL = {{Journal of Medical Imaging}},
PUBLISHER = {{SPIE Digital Library}},
VOLUME = {10},
NUMBER = {3},
PAGES = {034502},
YEAR = {2023},
DOI = {10.1117/1.JMI.10.3.034502},
KEYWORDS = {ultrasound imaging ; representation learning ; deep clustering ; semi-supervised learning},
PDF = {https://inria.hal.science/hal-03773082v3/file/soumission_jmi_dadoun_vf.pdf},
HAL_ID = {hal-03773082},
HAL_VERSION = {v3},

}


  8. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. Journal of Machine Learning Research, 24:1-43, March 2023. Note: Code is available https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL. 
    @article{fraboni:hal-03720629,
TITLE = {{A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates}},
AUTHOR = {Fraboni, Yann and Vidal, Richard and Kameni, Laetitia and Lorenzi, Marco},
url-hal= {https://hal.science/hal-03720629},
NOTE = {Code is available https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous\_FL},
JOURNAL = {{Journal of Machine Learning Research}},
PUBLISHER = {{Microtome Publishing}},
VOLUME = {24},
PAGES = {1-43},
YEAR = {2023},
MONTH = Mar,
PDF = {https://hal.science/hal-03720629v1/file/A_General_Theory_for_Federated_Optimization_with_Delayed_Gradients_and_Heterogeneous_Data.pdf},
HAL_ID = {hal-03720629},
HAL_VERSION = {v1},

}


  9. Nicolas Guigui, Nina Miolane, and Xavier Pennec. Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats. Foundations and Trends in Machine Learning, 16(3):329-493, February 2023. Keyword(s): Riemannian Geometry, Geometric Statistics, Python Library. 
    @article{guigui:hal-03766900,
TITLE = {{Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats}},
AUTHOR = {Guigui, Nicolas and Miolane, Nina and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-03766900},
JOURNAL = {{Foundations and Trends in Machine Learning}},
PUBLISHER = {{Now Publishers}},
VOLUME = {16},
NUMBER = {3},
PAGES = {329-493},
YEAR = {2023},
MONTH = Feb,
DOI = {10.1561/2200000098},
KEYWORDS = {Riemannian Geometry ; Geometric Statistics ; Python Library},
PDF = {https://inria.hal.science/hal-03766900v2/file/main-now.pdf},
HAL_ID = {hal-03766900},
HAL_VERSION = {v2},

}


  10. Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, Nicholas Ayache, and Hervé Delingette. Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO). Journal of Machine Learning for Biomedical Imaging, 2(UNSURE 2022 Special Issue):361-389, September 2023. Keyword(s): Consensus, Distance, Heuristic, Optimization, STAPLE. 
    @article{hamzaoui:hal-04208018,
TITLE = {{Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)}},
AUTHOR = {Hamzaoui, Dimitri and Montagne, Sarah and Renard-Penna, Rapha{\"e}le and Ayache, Nicholas and Delingette, Herv{\'e}},
url-hal= {https://hal.science/hal-04208018},
JOURNAL = {{Journal of Machine Learning for Biomedical Imaging}},
PUBLISHER = {{Melba editors}},
VOLUME = {2},
NUMBER = {UNSURE 2022 Special Issue},
PAGES = {361-389},
YEAR = {2023},
MONTH = Sep,
DOI = {10.59275/j.melba.2023-219c},
KEYWORDS = {Consensus ; Distance ; Heuristic ; Optimization ; STAPLE},
PDF = {https://hal.science/hal-04208018v1/file/MOJITO_MELBA.pdf},
HAL_ID = {hal-04208018},
HAL_VERSION = {v1},

}


  11. Philipp Harms, Peter W. Michor, Xavier Pennec, and Stefan Sommer. Geometry of Sample Spaces. Differential Geometry and its Applications, 90:102029, October 2023. Note: 29 pages, 1 figure. 
    @article{harms:hal-02972385,
TITLE = {{Geometry of Sample Spaces}},
AUTHOR = {Harms, Philipp and Michor, Peter W. and Pennec, Xavier and Sommer, Stefan},
url-hal= {https://inria.hal.science/hal-02972385},
NOTE = {29 pages, 1 figure},
JOURNAL = {{Differential Geometry and its Applications}},
PUBLISHER = {{Elsevier}},
VOLUME = {90},
PAGES = {102029},
YEAR = {2023},
MONTH = Oct,
DOI = {10.1016/j.difgeo.2023.102029},
HAL_ID = {hal-02972385},
HAL_VERSION = {v1},

}


  12. Raabid Hussain, Attila Frater, Roger Calixto, Chadlia Karoui, Jan Margeta, Zihao Wang, Michel Hoen, Hervé Delingette, François Patou, Charles Raffaelli, Clair Vandersteen, and Nicolas Guevara. Anatomical Variations of the Human Cochlea Using an Image Analysis Tool. Journal of Clinical Medicine, 12(2):509, January 2023. Keyword(s): Otology, Cochlea Modeling, Medical Image Analysis, cochlear morphology, cochlear implantation, statistical analysis. 
    @article{hussain:hal-04388909,
TITLE = {{Anatomical Variations of the Human Cochlea Using an Image Analysis Tool}},
AUTHOR = {Hussain, Raabid and Frater, Attila and Calixto, Roger and Karoui, Chadlia and Margeta, Jan and Wang, Zihao and Hoen, Michel and Delingette, Herv{\'e} and Patou, Fran{\c c}ois and Raffaelli, Charles and Vandersteen, Clair and Guevara, Nicolas},
url-hal= {https://inria.hal.science/hal-04388909},
JOURNAL = {{Journal of Clinical Medicine}},
PUBLISHER = {{MDPI}},
VOLUME = {12},
NUMBER = {2},
PAGES = {509},
YEAR = {2023},
MONTH = Jan,
DOI = {10.3390/jcm12020509},
KEYWORDS = {Otology ; Cochlea Modeling ; Medical Image Analysis ; cochlear morphology ; cochlear implantation ; statistical analysis},
PDF = {https://inria.hal.science/hal-04388909v1/file/jcm-12-00509-v2.pdf},
HAL_ID = {hal-04388909},
HAL_VERSION = {v1},

}


  13. Victoriya Kashtanova, Mihaela Pop, Ibrahim Ayed, Patrick Gallinari, and Maxime Sermesant. Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning. Interface Focus, 13(6), December 2023. Keyword(s): Physics-based learning, Deep Learning, Cardiac electrophysiology, Simulations. 
    @article{kashtanova:hal-04359753,
TITLE = {{Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning}},
AUTHOR = {Kashtanova, Victoriya and Pop, Mihaela and Ayed, Ibrahim and Gallinari, Patrick and Sermesant, Maxime},
url-hal= {https://hal.science/hal-04359753},
JOURNAL = {{Interface Focus}},
PUBLISHER = {{Royal Society publishing}},
VOLUME = {13},
NUMBER = {6},
YEAR = {2023},
MONTH = Dec,
DOI = {10.1098/rsfs.2023.0043},
KEYWORDS = {Physics-based learning ; Deep Learning ; Cardiac electrophysiology ; Simulations},
PDF = {https://hal.science/hal-04359753v1/file/Article_RoyalSociety_final%20%282%29.pdf},
HAL_ID = {hal-04359753},
HAL_VERSION = {v1},

}


  14. Fabien Lareyre, Kak Khee Yeung, Lisa Guzzi, Gilles Di Lorenzo, Arindam Chaudhuri, Christian-Alexander Behrendt, Konstantinos Spanos, and Juliette Raffort. Artificial intelligence in vascular surgical decision making. Seminars in Vascular Surgery, 36(3):448-453, September 2023. Keyword(s): Artificial intelligence, Machine learning, Vascular disease, Decision making, Precision medicine, Artificial intelligence. 
    @article{lareyre:hal-04361618,
TITLE = {{Artificial intelligence in vascular surgical decision making}},
AUTHOR = {Lareyre, Fabien and Yeung, Kak Khee and Guzzi, Lisa and Di Lorenzo, Gilles and Chaudhuri, Arindam and Behrendt, Christian-Alexander and Spanos, Konstantinos and Raffort, Juliette},
url-hal= {https://inria.hal.science/hal-04361618},
JOURNAL = {{Seminars in Vascular Surgery}},
PUBLISHER = {{Elsevier}},
VOLUME = {36},
NUMBER = {3},
PAGES = {448-453},
YEAR = {2023},
MONTH = Sep,
DOI = {10.1053/j.semvascsurg.2023.05.004},
KEYWORDS = {Artificial intelligence ; Machine learning ; Vascular disease ; Decision making ; Precision medicine ; Artificial intelligence},
HAL_ID = {hal-04361618},
HAL_VERSION = {v1},

}


  15. Sébastien Molière, Dimitri Hamzaoui, Anna Luzurier, Benjamin Granger, Sarah Montagne, Alexandre Allera, Malek Ezziane, Raphaële Quint, Mehdi Kalai, Nicholas Ayache, Hervé Delingette, and Raphaële Renard-Penna. Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI. Diagnostic and Interventional Imaging, August 2023. Keyword(s): Artificial intelligence Inter-reader variability Magnetic resonance imaging Prostate Segmentation. 
    @article{moliere:hal-04185065,
TITLE = {{Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI}},
AUTHOR = {Moli{\`e}re, S{\'e}bastien and Hamzaoui, Dimitri and Luzurier, Anna and Granger, Benjamin and Montagne, Sarah and Allera, Alexandre and Ezziane, Malek and Quint, Rapha{\"e}le and Kalai, Mehdi and Ayache, Nicholas and Delingette, Herv{\'e} and Renard-Penna, Rapha{\"e}le},
url-hal= {https://hal.science/hal-04185065},
JOURNAL = {{Diagnostic and Interventional Imaging}},
PUBLISHER = {{Elsevier}},
YEAR = {2023},
MONTH = Aug,
DOI = {10.1016/j.diii.2023.08.001},
KEYWORDS = {Artificial intelligence Inter-reader variability Magnetic resonance imaging Prostate Segmentation},
HAL_ID = {hal-04185065},
HAL_VERSION = {v1},

}


  16. Théodore Soulier, Olivier Colliot, Nicholas Ayache, and Benjamin Rohaut. How will tomorrow's algorithms fuse multimodal data? The example of the neuroprognosis in Intensive Care. Anaesthesia Critical Care & Pain Medicine, pp 101301, September 2023. Keyword(s): Disorders of Consciousness, Neurological Prognosis, Multimodal Data, Artificial Intelligence, Disorders of Consciousness Neurological Prognosis Multimodal Data Artificial Intelligence. 
    @article{soulier:hal-04227434,
TITLE = {{How will tomorrow's algorithms fuse multimodal data? The example of the neuroprognosis in Intensive Care}},
AUTHOR = {Soulier, Th{\'e}odore and Colliot, Olivier and Ayache, Nicholas and Rohaut, Benjamin},
url-hal= {https://hal.sorbonne-universite.fr/hal-04227434},
JOURNAL = {{Anaesthesia Critical Care \& Pain Medicine}},
PUBLISHER = {{Elsevier Masson}},
PAGES = {101301},
YEAR = {2023},
MONTH = Sep,
DOI = {10.1016/j.accpm.2023.101301},
KEYWORDS = {Disorders of Consciousness ; Neurological Prognosis ; Multimodal Data ; Artificial Intelligence ; Disorders of Consciousness Neurological Prognosis Multimodal Data Artificial Intelligence},
PDF = {https://hal.sorbonne-universite.fr/hal-04227434v1/file/manuscript4hal.pdf},
HAL_ID = {hal-04227434},
HAL_VERSION = {v1},

}


  17. Yann Thanwerdas and Xavier Pennec. Bures-Wasserstein minimizing geodesics between covariance matrices of different ranks. SIAM Journal on Matrix Analysis and Applications, 44(3):1447-1476, September 2023. Keyword(s): Covariance matrices, PSD matrices, Bures-Wasserstein, Orbit space, Geodesics, Injection domain, 15B48, 15A63, 53B20, 53C22, 58D17, 53-08, 53A04, 54E50, 58A35. 
    @article{thanwerdas:hal-03647321,
TITLE = {{Bures-Wasserstein minimizing geodesics between covariance matrices of different ranks}},
AUTHOR = {Thanwerdas, Yann and Pennec, Xavier},
url-hal= {https://hal.science/hal-03647321},
JOURNAL = {{SIAM Journal on Matrix Analysis and Applications}},
PUBLISHER = {{Society for Industrial and Applied Mathematics}},
VOLUME = {44},
NUMBER = {3},
PAGES = {1447-1476},
YEAR = {2023},
MONTH = Sep,
DOI = {10.1137/22M149168X},
KEYWORDS = {Covariance matrices ; PSD matrices ; Bures-Wasserstein ; Orbit space ; Geodesics ; Injection domain ; 15B48 ; 15A63 ; 53B20 ; 53C22 ; 58D17 ; 53-08 ; 53A04 ; 54E50 ; 58A35},
PDF = {https://hal.science/hal-03647321v1/file/main.pdf},
HAL_ID = {hal-03647321},
HAL_VERSION = {v1},

}


  18. Yann Thanwerdas and Xavier Pennec. O(n)-invariant Riemannian metrics on SPD matrices. Linear Algebra and its Applications, 661:163-201, March 2023. Keyword(s): 53B20, Symmetric Positive Definite matrices, Riemannian geometry, Invariance under orthogonal transformations, Families of metrics, Log-Euclidean metric, Affine-invariant metric, Bures-Wasserstein metric, Kernel metrics. 
    @article{thanwerdas:hal-03338601,
TITLE = {{O(n)-invariant Riemannian metrics on SPD matrices}},
AUTHOR = {Thanwerdas, Yann and Pennec, Xavier},
url-hal= {https://hal.science/hal-03338601},
JOURNAL = {{Linear Algebra and its Applications}},
PUBLISHER = {{Elsevier}},
VOLUME = {661},
PAGES = {163-201},
YEAR = {2023},
MONTH = Mar,
DOI = {10.1016/j.laa.2022.12.009},
KEYWORDS = {53B20 ; Symmetric Positive Definite matrices ; Riemannian geometry ; Invariance under orthogonal transformations ; Families of metrics ; Log-Euclidean metric ; Affine-invariant metric ; Bures-Wasserstein metric ; Kernel metrics},
PDF = {https://hal.science/hal-03338601v3/file/1-s2.0-S0024379522004360-main.pdf},
HAL_ID = {hal-03338601},
HAL_VERSION = {v3},

}


  19. Paul Tourniaire, Marius Ilie, Paul Hofman, Nicholas Ayache, and Hervé Delingette. MS-CLAM: Mixed Supervision for the classification and localization of tumors in Whole Slide Images. Medical Image Analysis, 85:102763, 2023. Keyword(s): Digital Pathology, Mixed Supervision, Deep Learning, Camelyon16, DigestPath2019. 
    @article{tourniaire:hal-03972289,
TITLE = {{MS-CLAM: Mixed Supervision for the classification and localization of tumors in Whole Slide Images}},
AUTHOR = {Tourniaire, Paul and Ilie, Marius and Hofman, Paul and Ayache, Nicholas and Delingette, Herv{\'e}},
url-hal= {https://inria.hal.science/hal-03972289},
JOURNAL = {{Medical Image Analysis}},
PUBLISHER = {{Elsevier}},
VOLUME = {85},
PAGES = {102763},
YEAR = {2023},
DOI = {10.1016/j.media.2023.102763},
KEYWORDS = {Digital Pathology ; Mixed Supervision ; Deep Learning ; Camelyon16 ; DigestPath2019},
PDF = {https://inria.hal.science/hal-03972289v1/file/article_MedIA_hal_version.pdf},
HAL_ID = {hal-03972289},
HAL_VERSION = {v1},

}


  20. Zihao Wang, Zhifei Xu, Jiayi He, Herve Delingette, and Jun Fan. Long Short-Term Memory Neural Equalizer. IEEE Transactions on Signal and Power Integrity, 2:13-22, 2023. 
    @article{wang:hal-04042921,
TITLE = {{Long Short-Term Memory Neural Equalizer}},
AUTHOR = {Wang, Zihao and Xu, Zhifei and He, Jiayi and Delingette, Herve and Fan, Jun},
url-hal= {https://inria.hal.science/hal-04042921},
JOURNAL = {{IEEE Transactions on Signal and Power Integrity}},
PUBLISHER = {{Institute of Electrical and Electronics Engineers}},
VOLUME = {2},
PAGES = {13-22},
YEAR = {2023},
DOI = {10.1109/TSIPI.2023.3242855},
HAL_ID = {hal-04042921},
HAL_VERSION = {v1},

}


  21. Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L Aguila, and Andre Altmann. Integration of Multimodal Data. In Olivier Colliot, editor, Machine Learning for Brain Disorders, volume NM197 of Neuromethods, pages 573 - 597. Springer, 2023. Keyword(s): Multivariate analysis, Latent variable models, Multimodal imaging, -Omics, Imaginggenetics, Partial least squares, Canonical correlation analysis, Variational autoencoders, Sparsity, Interpretability. 
    @incollection{lorenzi:hal-04239814,
TITLE = {{Integration of Multimodal Data}},
AUTHOR = {Lorenzi, Marco and Deprez, Marie and Balelli, Irene and Aguila, Ana L and Altmann, Andre},
url-hal= {https://hal.science/hal-04239814},
BOOKTITLE = {{Machine Learning for Brain Disorders}},
EDITOR = {Olivier Colliot},
PUBLISHER = {{Springer}},
SERIES = {Neuromethods},
VOLUME = {NM197},
PAGES = {573 - 597},
YEAR = {2023},
DOI = {10.1007/978-1-0716-3195-9\_19},
KEYWORDS = {Multivariate analysis ; Latent variable models ; Multimodal imaging ; -Omics ; Imaginggenetics ; Partial least squares ; Canonical correlation analysis ; Variational autoencoders ; Sparsity ; Interpretability},
PDF = {https://hal.science/hal-04239814v1/file/Chapter19.pdf},
HAL_ID = {hal-04239814},
HAL_VERSION = {v1},

}


  22. Buntheng Ly, Mihaela Pop, Hubert Cochet, Nicolas Duchateau, Declan O'regan, and Maxime Sermesant. Outcome Prediction. In AI and Big Data in Cardiology : A Practical Guide, pages 105-133. Springer International Publishing, May 2023. 
    @incollection{ly:hal-04212068,
TITLE = {{Outcome Prediction}},
AUTHOR = {Ly, Buntheng and Pop, Mihaela and Cochet, Hubert and Duchateau, Nicolas and O'regan, Declan and Sermesant, Maxime},
url-hal= {https://hal.science/hal-04212068},
BOOKTITLE = {{AI and Big Data in Cardiology : A Practical Guide}},
PUBLISHER = {{Springer International Publishing}},
PAGES = {105-133},
YEAR = {2023},
MONTH = May,
DOI = {10.1007/978-3-031-05071-8\_6},
PDF = {https://hal.science/hal-04212068v1/file/Book_2023_Chap6.pdf},
HAL_ID = {hal-04212068},
HAL_VERSION = {v1},

}


Conference articles

  1. Morten Akhoj, Xavier Pennec, and Stefan Sommer. Tangent phylogenetic PCA. In Scandinavian Conference on Image Analysis 2023, volume 13886 of Lecture Notes in Computer Science, Levi Ski Resort (Lapland), Finland, pages 77-90, April 2023. Springer Nature Switzerland. 
    @inproceedings{akhoj:hal-03842847,
TITLE = {{Tangent phylogenetic PCA}},
AUTHOR = {Akh{{\o}}j, Morten and Pennec, Xavier and Sommer, Stefan},
url-hal= {https://inria.hal.science/hal-03842847},
BOOKTITLE = {{Scandinavian Conference on Image Analysis 2023}},
ADDRESS = {Levi Ski Resort (Lapland), Finland},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {13886},
PAGES = {77-90},
YEAR = {2023},
MONTH = Apr,
DOI = {10.1007/978-3-031-31438-4\_6},
PDF = {https://inria.hal.science/hal-03842847v1/file/2208.12730.pdf},
HAL_ID = {hal-03842847},
HAL_VERSION = {v1},

}


  2. Safaa Al-Ali, Jordi Llopis-Lorente, Maria Teresa Mora, Maxime Sermesant, Beatriz Trénor, and Irene Balelli. A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment. In 2023 Computing in Cardiology (CinC), 2023 Computing in Cardiology (CinC), Atlanta (GA), United States, October 2023. Keyword(s): Causal discovery, Drug safety, Ions channel, TdP risk. 
    @inproceedings{alali:hal-04105144,
TITLE = {{A causal discovery approach for streamline ion channels selection to improve drug-induced TdP risk assessment}},
AUTHOR = {Al-Ali, Safaa and Llopis-Lorente, Jordi and Teresa Mora, Maria and Sermesant, Maxime and Tr{\'e}nor, Beatriz and Balelli, Irene},
url-hal= {https://hal.science/hal-04105144},
BOOKTITLE = {{2023 Computing in Cardiology (CinC)}},
ADDRESS = {Atlanta (GA), United States},
SERIES = {2023 Computing in Cardiology (CinC)},
YEAR = {2023},
MONTH = Oct,
DOI = {10.22489/CinC.2023.009},
KEYWORDS = {Causal discovery ; Drug safety ; Ions channel ; TdP risk},
PDF = {https://hal.science/hal-04105144v1/file/Draft_CMSB_Sept_2023-2.pdf},
HAL_ID = {hal-04105144},
HAL_VERSION = {v1},

}


  3. Anna Calissano, Elodie Maignant, and Xavier Pennec. Towards Quotient Barycentric Subspaces. In GSI 2023: Geometric Science of Information, volume 14071 of Lecture Notes in Computer Science, Saint-Malo (France), France, pages 366-374, August 2023. Springer Nature Switzerland. Keyword(s): Discrete Group, Quotient Space, Barycentric Subspace Analysis, Graph Space, Object Oriented Data Analysis. 
    @inproceedings{calissano:hal-04162647,
TITLE = {{Towards Quotient Barycentric Subspaces}},
AUTHOR = {Calissano, Anna and Maignant, Elodie and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04162647},
BOOKTITLE = {{GSI 2023: Geometric Science of Information}},
ADDRESS = {Saint-Malo (France), France},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {14071},
PAGES = {366-374},
YEAR = {2023},
MONTH = Aug,
DOI = {10.1007/978-3-031-38271-0\_36},
KEYWORDS = {Discrete Group ; Quotient Space ; Barycentric Subspace Analysis ; Graph Space ; Object Oriented Data Analysis},
PDF = {https://inria.hal.science/hal-04162647v1/file/Towards_Quotient_Barycentric_Subspaces.pdf},
HAL_ID = {hal-04162647},
HAL_VERSION = {v1},

}


  4. Nicolas Cedilnik and Jean-Marc Peyrat. Weighted tissue thickness. In FIMH 2023 - 12th International Conference On Functional Imaging And Modeling Of The Heart, Lecture Notes in Computer Science, Lyon, France, June 2023. Springer. Keyword(s): Thickness, Thickness measurements, Medical image analysis. 
    @inproceedings{cedilnik:hal-04111276,
TITLE = {{Weighted tissue thickness}},
AUTHOR = {Cedilnik, Nicolas and Peyrat, Jean-Marc},
url-hal= {https://inria.hal.science/hal-04111276},
BOOKTITLE = {{FIMH 2023 - 12th International Conference On Functional Imaging And Modeling Of The Heart}},
ADDRESS = {Lyon, France},
PUBLISHER = {{Springer}},
SERIES = {Lecture Notes in Computer Science},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {Thickness ; Thickness measurements ; Medical image analysis},
PDF = {https://inria.hal.science/hal-04111276v1/file/article.pdf},
HAL_ID = {hal-04111276},
HAL_VERSION = {v1},

}


  5. Hind Dadoun, Hervé Delingette, Anne-Laure Rousseau, Eric de Kerviler, and Nicholas Ayache. Joint representation learning from french radiological reports and ultrasound images. In IEEE ISBI 2023 - International Symposium on Biomedical Imaging, Cartagena de Indias, Colombia, April 2023. IEEE. Keyword(s): multimodal learning deep clustering natural language processing ultrasound examinations kidneys, multimodal learning, deep clustering, natural language processing, ultrasound examinations, kidneys. 
    @inproceedings{dadoun:hal-03984528,
TITLE = {{Joint representation learning from french radiological reports and ultrasound images}},
AUTHOR = {Dadoun, Hind and Delingette, Herv{\'e} and Rousseau, Anne-Laure and de Kerviler, Eric and Ayache, Nicholas},
url-hal= {https://inria.hal.science/hal-03984528},
BOOKTITLE = {{IEEE ISBI 2023 - International Symposium on Biomedical Imaging}},
ADDRESS = {Cartagena de Indias, Colombia},
ORGANIZATION = {{IEEE}},
YEAR = {2023},
MONTH = Apr,
KEYWORDS = {multimodal learning deep clustering natural language processing ultrasound examinations kidneys ; multimodal learning ; deep clustering ; natural language processing ; ultrasound examinations ; kidneys},
PDF = {https://inria.hal.science/hal-03984528v1/file/HIND_DADOUN_ISBI_2023.pdf},
HAL_ID = {hal-03984528},
HAL_VERSION = {v1},

}


  6. Zhijie Fang, Hervé Delingette, and Nicholas Ayache. Anatomical Landmark Detection for Initializing US and MR Image Registration. In MICCAI ASMUS 2023 - 4th International Workshop of Advances in Simplifying Medical UltraSound - a workshop held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, Canada, October 2023. Keyword(s): Landmark detection, Image-guided intervention, Convolutional neural network and Prostate cancer. 
    @inproceedings{fang:hal-04189905,
TITLE = {{Anatomical Landmark Detection for Initializing US and MR Image Registration}},
AUTHOR = {Fang, Zhijie and Delingette, Herv{\'e} and Ayache, Nicholas},
url-hal= {https://hal.science/hal-04189905},
BOOKTITLE = {{MICCAI ASMUS 2023 - 4th International Workshop of Advances in Simplifying Medical UltraSound - a workshop held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer Assisted Intervention}},
ADDRESS = {Vancouver, Canada},
YEAR = {2023},
MONTH = Oct,
KEYWORDS = {Landmark detection ; Image-guided intervention ; Convolutional neural network and Prostate cancer},
PDF = {https://hal.science/hal-04189905v1/file/Fang-ASMUS-camera-ready-version-18Aug2023.pdf},
HAL_ID = {hal-04189905},
HAL_VERSION = {v1},

}


  7. Yann Fraboni, Lucia Innocenti, Michela Antonelli, Richard Vidal, Laetitia Kameni, Sebastien Ourselin, and Marco Lorenzi. Validation of Federated Unlearning on Collaborative Prostate Segmentation. In DECAF MICCAI 2023 Workshops, volume 14393 of Lecture Notes in Computer Science, Toronto, Canada, pages 322-333, October 2023. Medical Image Computing and Computer Assisted Intervention, Springer Nature Switzerland. Keyword(s): federated unlearning, prostate cancer, segmentation, Medical imaging. 
    @inproceedings{fraboni:hal-04417106,
TITLE = {{Validation of~Federated Unlearning on~Collaborative Prostate Segmentation}},
AUTHOR = {Fraboni, Yann and Innocenti, Lucia and Antonelli, Michela and Vidal, Richard and Kameni, Laetitia and Ourselin, Sebastien and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-04417106},
BOOKTITLE = {{DECAF MICCAI 2023 Workshops}},
ADDRESS = {Toronto, Canada},
ORGANIZATION = {{Medical Image Computing and Computer Assisted Intervention}},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {14393},
PAGES = {322-333},
YEAR = {2023},
MONTH = Oct,
DOI = {10.1007/978-3-031-47401-9\_31},
KEYWORDS = {federated unlearning ; prostate cancer ; segmentation ; Medical imaging},
PDF = {https://inria.hal.science/hal-04417106v1/file/DeCaf___Federated_Unlearning_of_Prostate_Segmentation___vf-1.pdf},
HAL_ID = {hal-04417106},
HAL_VERSION = {v1},

}


  8. Etrit Haxholli and Marco Lorenzi. Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows. In DLDE-III Workshop in the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, United States, December 2023. 
    @inproceedings{haxholli:hal-03911870,
TITLE = {{Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows}},
AUTHOR = {Haxholli, Etrit and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-03911870},
BOOKTITLE = {{DLDE-III Workshop in the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)}},
ADDRESS = {New Orleans, Louisiana, United States},
YEAR = {2023},
MONTH = Dec,
PDF = {https://inria.hal.science/hal-03911870v2/file/Enhanced_distribution_modelling_AFFJORD_HAL.pdf},
HAL_ID = {hal-03911870},
HAL_VERSION = {v2},

}


  9. Etrit Haxholli and Marco Lorenzi. Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching. In NeurIPS 2023 Workshop on Diffusion Models, New Orleans, Louisiana, United States, December 2023. 
    @inproceedings{haxholli:hal-04032669,
TITLE = {{Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching}},
AUTHOR = {Haxholli, Etrit and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-04032669},
BOOKTITLE = {{NeurIPS 2023 Workshop on Diffusion Models}},
ADDRESS = {New Orleans, Louisiana, United States},
YEAR = {2023},
MONTH = Dec,
PDF = {https://inria.hal.science/hal-04032669v1/file/Improving_Training_Speed_and_Density_Estimation_of_Diffusion_Models_via_Parallel_Score_Matching__HAL_.pdf.pdf},
HAL_ID = {hal-04032669},
HAL_VERSION = {v1},

}


  10. Lucia Innocenti, Michela Antonelli, Francesco Cremonesi, Kenaan Sarhan, Alejandro Granados, Vicky Goh, Sebastien Ourselin, and Marco Lorenzi. Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation. In ECML - PharML - Applications of Machine Learning in Pharma and Healthcare (Workshop at ECML PKDD 2023), Turin (IT), Italy, September 2023. arXiv. Note: Workshop at ECML PKDD 2023. Keyword(s): Collaborative Learning, Cost-Effectiveness, Prostate Segmentation. 
    @inproceedings{innocenti:hal-04357349,
TITLE = {{Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation}},
AUTHOR = {Innocenti, Lucia and Antonelli, Michela and Cremonesi, Francesco and Sarhan, Kenaan and Granados, Alejandro and Goh, Vicky and Ourselin, Sebastien and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-04357349},
NOTE = {Workshop at ECML PKDD 2023},
BOOKTITLE = {{ECML - PharML - Applications of Machine Learning in Pharma and Healthcare (Workshop at ECML PKDD 2023)}},
ADDRESS = {Turin (IT), Italy},
PUBLISHER = {{arXiv}},
YEAR = {2023},
MONTH = Sep,
DOI = {10.48550/arXiv.2309.17097},
KEYWORDS = {Collaborative Learning ; Cost-Effectiveness ; Prostate Segmentation},
PDF = {https://inria.hal.science/hal-04357349v1/file/ConsensusBenchmarkV2.pdf},
HAL_ID = {hal-04357349},
HAL_VERSION = {v1},

}


  11. Elodie Maignant, Alain Trouvé, and Xavier Pennec. Riemannian Locally Linear Embedding with Application to Kendall Shape Spaces. In GSI 2023: Geometric Science of Information, volume 14071 of Lecture Notes in Computer Science, Saint-Malo, (France), France, pages 12-20, August 2023. Springer Nature Switzerland. Keyword(s): Locally Linear Embedding, Optimisation on Quotient Manifolds, Shape Spaces. 
    @inproceedings{maignant:hal-04122754,
TITLE = {{Riemannian Locally Linear Embedding with Application to Kendall Shape Spaces}},
AUTHOR = {Maignant, Elodie and Trouv{\'e}, Alain and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04122754},
BOOKTITLE = {{GSI 2023: Geometric Science of Information}},
ADDRESS = {Saint-Malo, (France), France},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {14071},
PAGES = {12-20},
YEAR = {2023},
MONTH = Aug,
DOI = {10.1007/978-3-031-38271-0\_2},
KEYWORDS = {Locally Linear Embedding ; Optimisation on Quotient Manifolds ; Shape Spaces},
PDF = {https://inria.hal.science/hal-04122754v1/file/Riemannian_Locally_Linear_Embedding_with_Application_to_Kendall_Shape_Spaces.pdf},
HAL_ID = {hal-04122754},
HAL_VERSION = {v1},

}


  12. Hari Sreedhar, Guillaume P R Lajoinie, Charles Raffaelli, and Hervé Delingette. Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset. In DALI 2023 - Data Augmentation, Labelling, and Imperfections / MICCAI Workshop 2023, MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2023, held in conjunction with MICCAI 2023 Proceedings, Vancouver, Canada, October 2023. Keyword(s): Thyroid cancer, Active learning, Ultrasound imaging. 
    @inproceedings{sreedhar:hal-04209622,
TITLE = {{Active Learning Strategies on a Real-World Thyroid Ultrasound Dataset}},
AUTHOR = {Sreedhar, Hari and Lajoinie, Guillaume P R and Raffaelli, Charles and Delingette, Herv{\'e}},
url-hal= {https://inria.hal.science/hal-04209622},
BOOKTITLE = {{DALI 2023 - Data Augmentation, Labelling, and Imperfections / MICCAI Workshop 2023}},
ADDRESS = {Vancouver, Canada},
SERIES = {MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2023, held in conjunction with MICCAI 2023 Proceedings},
YEAR = {2023},
MONTH = Oct,
KEYWORDS = {Thyroid cancer ; Active learning ; Ultrasound imaging},
PDF = {https://inria.hal.science/hal-04209622v1/file/manuscript.pdf},
HAL_ID = {hal-04209622},
HAL_VERSION = {v1},

}


  13. Tom Szwagier and Xavier Pennec. Rethinking the Riemannian Logarithm on Flag Manifolds as an Orthogonal Alignment Problem. In GSI 2023: Geometric Science of Information, volume 14071 of Lecture Notes in Computer Science, Saint-Malo, (France), France, pages 375-383, August 2023. Springer Nature Switzerland. Keyword(s): Flag manifolds, Riemannian logarithm, Orthogonal alignment, Procrustes analysis, Flag manifolds Riemannian logarithm Orthogonal alignment Procrustes analysis. 
    @inproceedings{szwagier:hal-04100534,
TITLE = {{Rethinking the Riemannian Logarithm on Flag Manifolds as an Orthogonal Alignment Problem}},
AUTHOR = {Szwagier, Tom and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04100534},
BOOKTITLE = {{GSI 2023: Geometric Science of Information}},
ADDRESS = {Saint-Malo, (France), France},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {14071},
PAGES = {375-383},
YEAR = {2023},
MONTH = Aug,
DOI = {10.1007/978-3-031-38271-0\_37},
KEYWORDS = {Flag manifolds ; Riemannian logarithm ; Orthogonal alignment ; Procrustes analysis ; Flag manifolds Riemannian logarithm Orthogonal alignment Procrustes analysis},
PDF = {https://inria.hal.science/hal-04100534v1/file/GSI-119.pdf},
HAL_ID = {hal-04100534},
HAL_VERSION = {v1},

}


  14. Yann Thanwerdas and Xavier Pennec. Characterization of Invariant Inner Products. In GSI 2023 - International Conference on Geometric Science of Information, volume LNCS-14071 of Geometric Science of Information: 6th International Conference, GSI 2023, St. Malo, France, August 30 -- September 1, 2023, Proceedings, Part I, Saint Malo, France, France, pages 384-391, August 2023. Springer Nature Switzerland. Keyword(s): Invariant inner product Invariant Riemannian metric Group action Representation theory, Invariant inner product, Invariant Riemannian metric, Group action, Representation theory. 
    @inproceedings{thanwerdas:hal-04229093,
TITLE = {{Characterization of Invariant Inner Products}},
AUTHOR = {Thanwerdas, Yann and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04229093},
BOOKTITLE = {{GSI 2023 - International Conference on Geometric Science of Information}},
ADDRESS = {Saint Malo, France, France},
PUBLISHER = {{Springer Nature Switzerland}},
SERIES = {Geometric Science of Information: 6th International Conference, GSI 2023, St. Malo, France, August 30 -- September 1, 2023, Proceedings, Part I},
VOLUME = {LNCS-14071},
PAGES = {384-391},
YEAR = {2023},
MONTH = Aug,
DOI = {10.1007/978-3-031-38271-0\_38},
KEYWORDS = {Invariant inner product Invariant Riemannian metric Group action Representation theory ; Invariant inner product ; Invariant Riemannian metric ; Group action ; Representation theory},
PDF = {https://inria.hal.science/hal-04229093v1/file/GSI-105.pdf},
HAL_ID = {hal-04229093},
HAL_VERSION = {v1},

}


  15. Yingyu Yang and Maxime Sermesant. Unsupervised Polyaffine Transformation Learning for Echocardiography Motion Estimation. In FIMH 2023 - The 12th International Conference on Functional Imaging and Modeling of The Heart, Lyon, France, June 2023. Keyword(s): Motion Estimation Echocardiography Polyaffine Transformation, Motion Estimation, Echocardiography, Polyaffine Transformation. 
    @inproceedings{yang:hal-04109721,
TITLE = {{Unsupervised Polyaffine Transformation Learning for Echocardiography Motion Estimation}},
AUTHOR = {Yang, Yingyu and Sermesant, Maxime},
url-hal= {https://inria.hal.science/hal-04109721},
BOOKTITLE = {{FIMH 2023 - The 12th International Conference on Functional Imaging and Modeling of The Heart}},
ADDRESS = {Lyon, France},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {Motion Estimation Echocardiography Polyaffine Transformation ; Motion Estimation ; Echocardiography ; Polyaffine Transformation},
PDF = {https://inria.hal.science/hal-04109721v1/file/Unsupervised_Polyaffine_Motion_Model_for_Echocardiography_Analysis__final_-2.pdf},
HAL_ID = {hal-04109721},
HAL_VERSION = {v1},

}


Internal reports

  1. Frédéric Blanqui, Anne Canteaut, Hidde De Jong, Sébastien Imperiale, Nathalie Mitton, Guillaume Pallez, Xavier Pennec, Xavier Rival, and Bertrand Thirion. Recommendations on ''Grey-Zone Publishers''. Technical report, Inria, January 2023. 
    @techreport{blanqui:hal-04201298,
TITLE = {{Recommendations on ''Grey-Zone Publishers''}},
AUTHOR = {Blanqui, Fr{\'e}d{\'e}ric and Canteaut, Anne and Jong, Hidde De and Imperiale, S{\'e}bastien and Mitton, Nathalie and Pallez, Guillaume and Pennec, Xavier and Rival, Xavier and Thirion, Bertrand},
url-hal= {https://inria.hal.science/hal-04201298},
PAGES = {1-3},
INSTITUTION = {{Inria}},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-04201298v1/file/CE_note-editeurs-douteux-EN.pdf},
HAL_ID = {hal-04201298},
HAL_VERSION = {v1},

}


  2. Frédéric Blanqui, Anne Canteaut, Hidde de Jong, Sébastien Imperiale, Nathalie Mitton, Guillaume Pallez, Xavier Pennec, Xavier Rival, and Bertrand Thirion. Recommandations sur les `` éditeurs de la zone grise ''. Technical report, Inria, January 2023. 
    @techreport{blanqui:hal-04001505,
TITLE = {{Recommandations sur les `` {\'e}diteurs de la zone grise ''}},
AUTHOR = {Blanqui, Fr{\'e}d{\'e}ric and Canteaut, Anne and de Jong, Hidde and Imperiale, S{\'e}bastien and Mitton, Nathalie and Pallez, Guillaume and Pennec, Xavier and Rival, Xavier and Thirion, Bertrand},
url-hal= {https://inria.hal.science/hal-04001505},
PAGES = {1-3},
INSTITUTION = {{Inria}},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-04001505v1/file/CE_note-editeurs-douteux.pdf},
HAL_ID = {hal-04001505},
HAL_VERSION = {v1},

}


Miscellaneous

  1. Andreas Abildtrup Hansen, Yanis Aeschlimann, Samuel Deslauriers-Gauthier, and Anna Calissano. Permutation equivariant structure-function mapping. 2023 Annual Meeting of the Organization for Human Brain Mapping, July 2023. Note: Poster. 
    @misc{abildtruphansen:hal-04390750,
TITLE = {{Permutation equivariant structure-function mapping}},
AUTHOR = {Abildtrup Hansen, Andreas and Aeschlimann, Yanis and Deslauriers-Gauthier, Samuel and Calissano, Anna},
url-hal= {https://hal.science/hal-04390750},
NOTE = {Poster},
HOWPUBLISHED = {{2023 Annual Meeting of the Organization for Human Brain Mapping}},
YEAR = {2023},
MONTH = Jul,
PDF = {https://hal.science/hal-04390750v1/file/OHBM2023_poster.pdf},
HAL_ID = {hal-04390750},
HAL_VERSION = {v1},

}


  2. Yanis Aeschlimann, Anna Calissano, Samuel Deslauriers-Gauthier, Théodore Papadopoulo, and Andreas Hansen. Preliminary results on the graph matching of structural and functional brain networks. Neuromod Yearly Meeting 2023, June 2023. Note: Poster. Keyword(s): brain networks, structural connectivity, functional connectivity, brain atlas, network alignment. 
    @misc{aeschlimann:hal-04165209,
TITLE = {{Preliminary results on the graph matching of structural and functional brain networks}},
AUTHOR = {Aeschlimann, Yanis and Calissano, Anna and Deslauriers-Gauthier, Samuel and Papadopoulo, Th{\'e}odore and Hansen, Andreas},
url-hal= {https://inria.hal.science/hal-04165209},
NOTE = {Poster},
HOWPUBLISHED = {{Neuromod Yearly Meeting 2023}},
YEAR = {2023},
MONTH = Jun,
KEYWORDS = {brain networks ; structural connectivity ; functional connectivity ; brain atlas ; network alignment},
PDF = {https://inria.hal.science/hal-04165209v1/file/Neuromod_2023_poster_Yanis_AESCHLIMANN.pdf},
HAL_ID = {hal-04165209},
HAL_VERSION = {v1},

}


  3. Anna Calissano, Théodore Papadopoulo, Xavier Pennec, and Samuel Deslauriers-Gauthier. Graph Matching improves the Similarity of Structural Brain Networks. OHBM 2023 - Organization for Human Brain Mapping, July 2023. Note: Poster. Keyword(s): Brain network, Network alignment, diffusion MRI. 
    @misc{calissano:hal-04390489,
TITLE = {{Graph Matching improves the Similarity of Structural Brain Networks}},
AUTHOR = {Calissano, Anna and Papadopoulo, Th{\'e}odore and Pennec, Xavier and Deslauriers-Gauthier, Samuel},
url-hal= {https://hal.science/hal-04390489},
NOTE = {Poster},
HOWPUBLISHED = {{OHBM 2023 - Organization for Human Brain Mapping}},
YEAR = {2023},
MONTH = Jul,
KEYWORDS = {Brain network ; Network alignment ; diffusion MRI},
PDF = {https://hal.science/hal-04390489v1/file/BrainMatching.pdf},
HAL_ID = {hal-04390489},
HAL_VERSION = {v1},

}


  4. Morten Akhoj, James Benn, Erlend Grong, Stefan Sommer, and Xavier Pennec. Principal subbundles for dimension reduction. Note: Working paper or preprint, July 2023. 
    @unpublished{akhoj:hal-04156036,
TITLE = {{Principal subbundles for dimension reduction}},
AUTHOR = {Akh{{\o}}j, Morten and Benn, James and Grong, Erlend and Sommer, Stefan and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04156036},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jul,
HAL_ID = {hal-04156036},
HAL_VERSION = {v1},

}


  5. Irene Balelli, Aude Sportisse, Francesco Cremonesi, Pierre-Alexandre Mattei, and Marco Lorenzi. Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. Note: Working paper or preprint, 2023. Keyword(s): Missing data, Federated learning, Federated pre-processing, Variational autoencoders, Deep Learning. 
    @unpublished{balelli:hal-04069795,
TITLE = {{Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models}},
AUTHOR = {Balelli, Irene and Sportisse, Aude and Cremonesi, Francesco and Mattei, Pierre-Alexandre and Lorenzi, Marco},
url-hal= {https://hal.science/hal-04069795},
NOTE = {working paper or preprint},
YEAR = {2023},
KEYWORDS = {Missing data ; Federated learning ; Federated pre-processing ; Variational autoencoders ; Deep Learning},
PDF = {https://hal.science/hal-04069795v1/file/main_MICCAI.pdf},
HAL_ID = {hal-04069795},
HAL_VERSION = {v1},

}


  6. Blanche Buet and Xavier Pennec. Flagfolds. Note: Working paper or preprint, 2023. 
    @unpublished{buet:hal-04112465,
TITLE = {{Flagfolds}},
AUTHOR = {Buet, Blanche and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04112465},
NOTE = {working paper or preprint},
YEAR = {2023},
HAL_ID = {hal-04112465},
HAL_VERSION = {v1},

}


  7. Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, and Marco Lorenzi. Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications. Note: Working paper or preprint, April 2023. Keyword(s): Machine learning, Biomedical Application, Healthcare, Federated Learning Framework. 
    @unpublished{cremonesi:hal-04081557,
TITLE = {{Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications}},
AUTHOR = {Cremonesi, Francesco and Vesin, Marc and Cansiz, Sergen and Bouillard, Yannick and Balelli, Irene and Innocenti, Lucia and Silva, Santiago and Ayed, Samy-Safwan and Taiello, Riccardo and Kameni, Laetita and Vidal, Richard and Orlhac, Fanny and Nioche, Christophe and Lapel, Nathan and Houis, Bastien and Modzelewski, Romain and Humbert, Olivier and {\"O}nen, Melek and Lorenzi, Marco},
url-hal= {https://inria.hal.science/hal-04081557},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Apr,
KEYWORDS = {Machine learning ; Biomedical Application ; Healthcare ; Federated Learning Framework},
PDF = {https://inria.hal.science/hal-04081557v1/file/fedbiomed_paper-5.pdf},
HAL_ID = {hal-04081557},
HAL_VERSION = {v1},

}


  8. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. Note: Working paper or preprint, January 2023. 
    @unpublished{fraboni:hal-03910848,
TITLE = {{Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization}},
AUTHOR = {Fraboni, Yann and Vidal, Richard and Kameni, Laetitia and Lorenzi, Marco},
url-hal= {https://hal.science/hal-03910848},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jan,
PDF = {https://hal.science/hal-03910848v1/file/Sequential_Informed_Federated_Unlearning.pdf},
HAL_ID = {hal-03910848},
HAL_VERSION = {v1},

}


  9. Dimbihery Rabenoro and Xavier Pennec. The geometry of Riemannian submersions from compact Lie groups. Application to flag manifolds. Note: Working paper or preprint, October 2023. 
    @unpublished{rabenoro:hal-04232479,
TITLE = {{The geometry of Riemannian submersions from compact Lie groups. Application to flag manifolds}},
AUTHOR = {Rabenoro, Dimbihery and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04232479},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Oct,
PDF = {https://inria.hal.science/hal-04232479v1/file/2302.14810-1.pdf},
HAL_ID = {hal-04232479},
HAL_VERSION = {v1},

}


  10. Tom Szwagier and Xavier Pennec. Stratified principal component analysis. Note: Working paper or preprint, November 2023. Keyword(s): Covariance model, Eigenvalue multiplicity, Flag manifold, Parsimony, Probabilistic principal component analysis, Stratification. 
    @unpublished{szwagier:hal-04171853,
TITLE = {{Stratified principal component analysis}},
AUTHOR = {Szwagier, Tom and Pennec, Xavier},
url-hal= {https://inria.hal.science/hal-04171853},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Nov,
KEYWORDS = {Covariance model ; Eigenvalue multiplicity ; Flag manifold ; Parsimony ; Probabilistic principal component analysis ; Stratification},
PDF = {https://inria.hal.science/hal-04171853v2/file/Stratified-PCA.pdf},
HAL_ID = {hal-04171853},
HAL_VERSION = {v2},

}



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