Publications of year 2023

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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



7. Morten Akhoj Pedersen. Riemannian and sub-Riemannian methods for dimension reduction. Theses, INRIA Sophia-Antipolis ; University of Copenhagen, November 2023. Keyword(s): Differential geometry, Riemannian geometry, Sub-Riemannian geometry, Geometric statistics, Mathematical statistics, Machine learning, Statistiques géométriques, Géométrie différentielle, Géométrie Riemannienne, Géométrie sous-Riemannienne, Statistique mathématique, Apprentissage automatique. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]

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. [bibtex-entry]



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. [bibtex-entry]



3. Anna Calissano, Théodore Papadopoulo, Xavier Pennec, and Samuel Deslauriers-Gauthier. Graph Alignment Exploiting the Spatial Organisation Improves the Similarity of Brain Networks. Human Brain Mapping, 45(1):e26554, December 2023. [bibtex-entry]



4. 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. [bibtex-entry]



5. 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. [bibtex-entry]



6. 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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]

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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]

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. [bibtex-entry]



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. [bibtex-entry]

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. [bibtex-entry]



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. [bibtex-entry]



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. [bibtex-entry]



4. Morten Akhoj, James Benn, Erlend Grong, Stefan Sommer, and Xavier Pennec. Principal subbundles for dimension reduction. Note: Working paper or preprint, July 2023. [bibtex-entry]



5. Safaa Al-Ali, John Chaussard, Sébastien Li-Thiao-Té, Éric Ogier-Denis, Alice Percy-Du-Sert, Xavier Treton, and Hatem Zaag. Detection of ulcerative colitis lesions from weakly annotated colonoscopy videos using bounding boxes. Note: Working paper or preprint, November 2023. [bibtex-entry]



6. 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. [bibtex-entry]



7. James Benn and Stephen Marsland. Towards a BCH Formula on the Diffeomorphism Group with a Right-Invariant Metric. Note: Working paper or preprint, December 2023. [bibtex-entry]



8. Blanche Buet and Xavier Pennec. Flagfolds. Note: Working paper or preprint, 2023. [bibtex-entry]



9. 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. [bibtex-entry]



10. 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. [bibtex-entry]



11. 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. [bibtex-entry]



12. 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. [bibtex-entry]



13. Yann Thanwerdas. Permutation-invariant log-Euclidean geometries on full-rank correlation matrices. Note: Working paper or preprint, November 2023. Keyword(s): SPD matrices, elliptope, correlation matrices, log-Euclidean metric, permutationinvariant, cor-inversion, off-log metric, log-scaled metric, quotient-affine metric AMS subject classifications.. [bibtex-entry]

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