Publications of Marco Lorenzi

1. Marco Lorenzi. Modelling pathological processes from heterogeneous and high-dimensional biomedical data. Habilitation à diriger des recherches, UCA, January 2020. Keyword(s): statistical learning, medical imaging, brain, machine learning, Alzheimer's disease, federated learning, genetics, apprentissage statistique, imageie medicale, cerveau, apprentissage par ordinateurs, maladie d'Alzheimer, apprentissge féderé genetics. [bibtex-entry]



2. Marco Lorenzi. Deformation-based morphometry of the brain for the development of surrogate markers in Alzheimer's disease. Ph.D. Thesis, University of Nice Sophia Antipolis, December 2012. [bibtex-entry]

1. Riccardo Taiello, Melek Önen, Francesco Capano, Olivier Humbert, and Marco Lorenzi. Privacy preserving image registration. Medical Image Analysis, 94, May 2024. [bibtex-entry]



2. 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]



3. 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]



4. Clément Abi Nader, Federica Ribaldi, Giovanni B Frisoni, Valentina Garibotto, Philippe Robert, Nicholas Ayache, and Marco Lorenzi. SimulAD: A dynamical model for personalized simulation and disease staging in Alzheimer's disease. Neurobiology of Aging, 113:73-83, May 2022. Keyword(s): Alzheimer's disease, Disease progression models, Clinical trials, Biomarkers. [bibtex-entry]



5. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. Journal of Machine Learning for Biomedical Imaging, April 2022. [bibtex-entry]



6. Etrit Haxholli and Marco Lorenzi. On Tail Decay Rate Estimation of Loss Function Distributions. Journal of Machine Learning Research, December 2022. Keyword(s): Extreme Value Theory, Tail Modelling, Loss Function Distributions, Peaks-Over-Threshold, Cross-Tail-Estimation, Model Ranking. [bibtex-entry]



7. M Nuñez-Garcia, S Finsterbach, Buntheng Ly, Marco Lorenzi, H Cochet, and Maxime Sermesant. Long-term remodelling and arrhythmogenicity after myocardial infarction using a novel image-based estimator: the Scar Maturation Score. EP-Europace, 24(Supplement_1), May 2022. [bibtex-entry]



8. 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. Brain Communications, February 2021. Keyword(s): Alzheimer's Disease, Clinical trials, Disease progression, Amyloid hypothesis, Mental State Examination, FAQ = Functional Assessment Questionnaire, RAVLT = Rey Auditory Verbal Learning Test, CDRSB = Clinical Dementia Rating Scale Sum of Boxes. [bibtex-entry]



9. Jaume Banus, Marco Lorenzi, Oscar Camara, and Maxime Sermesant. Biophysics-based statistical learning: Application to heart and brain interactions. Medical Image Analysis, 72, August 2021. Keyword(s): Lumped model, Cardiovascular modelling, Personalisation, White matter damage, Atrial fibrillation, Heart-Brain interaction. [bibtex-entry]



10. Adrià Casamitjana, Marco Lorenzi, Sebastiano Ferraris, Loïc Peter, Marc Modat, Allison Stevens, Bruce Fischl, Tom Vercauteren, and Juan Eugenio Iglesias. Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas. Medical Image Analysis, October 2021. Keyword(s): nonlinear registration, 3D reconstruction, linear programming, ex vivo MRI, histology. [bibtex-entry]



11. Marie Deprez, Julien Moreira, Maxime Sermesant, and Marco Lorenzi. Decoding genetic markers of multiple phenotypic layers through biologically constrained Genome-to-Phenome Bayesian Sparse Regression. Frontiers in Molecular Medicine, 2021. Keyword(s): Bayesian, Variational Dropout, Genome, Phenome, Regression, Biological constraint. [bibtex-entry]



12. Sara Garbarino and Marco Lorenzi. Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain. NeuroImage, 235:117980, July 2021. Keyword(s): Neurodegeneration, Causal model, Dynamical systems, Protein propagation, Gaussian process, Brain connectivity. [bibtex-entry]



13. Georgios Lazaridis, Marco Lorenzi, Jibran Mohamed-Noriega, Soledad Aguilar-Munoa, Katsuyoshi Suzuki, Hiroki Nomoto, Sebastien Ourselin, David Garway-Heath, David Crabb, Catey Bunce, Francesca Amalfitano, Nitin Anand, Augusto Azuara-Blanco, Rupert Bourne, David Broadway, Ian Cunliffe, Jeremy Diamond, Scott Fraser, Tuan Ho, Keith Martin, Andrew Mcnaught, Anil Negi, Ameet Shah, Paul Spry, Edward White, Richard Wormald, Wen Xing, and Thierry Zeyen. OCT Signal Enhancement with Deep Learning. Ophthalmology Glaucoma, 4(3):295-304, May 2021. Keyword(s): Deep learning, Glaucoma, Image analysis, OCT, Visual fields. [bibtex-entry]



14. Georgios Lazaridis, Marco Lorenzi, Sebastien Ourselin, and David Garway-Heath. Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks. Medical Image Analysis, 68:101906, February 2021. Keyword(s): Optical coherence tomography, Deep learning, Perceptual loss, GAN, Label fusion, Statistical power, Clinical trials, Glaucoma. [bibtex-entry]



15. 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. [bibtex-entry]



16. 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. [bibtex-entry]



17. Clement Abi Nader, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data. NeuroImage, 2019. Keyword(s): Stochastic variational inference, Clinical trials, Bayesian modeling, Alzheimer's disease, Disease progression modeling, Gaussian Process. [bibtex-entry]



18. Luigi Antelmi, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. Proceedings of Machine Learning Research, (97):302-311, 2019. [bibtex-entry]



19. Claire Cury, Stanley Durrleman, David Cash, Marco Lorenzi, Jennifer M Nicholas, Martina Bocchetta, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sébastien Ourselin, Jonathan Rohrer, Marc Modat, Christin Andersson, Silvana Archetti, Andrea Arighi, Luisa Benussi, Sandra Black, Maura Cosseddu, Marie Fallstrm, Carlos G. Ferreira, Chiara Fenoglio, Nick Fox, Morris Freedman, Giorgio Fumagalli, Stefano Gazzina, Robert Ghidoni, Marina Grisoli, Vesna Jelic, Lize Jiskoot, Ron Keren, Gemma Lombardi, Carolina Maruta, Lieke Meeter, Rick van Minkelen, Benedetta Nacmias, Linn Ijerstedt, Alessandro Padovani, Jessica Panman, Michela Pievani, Cristina Polito, Enrico Premi, Sara Prioni, Rosa Rademakers, Veronica Redaelli, Ekaterina Rogaeva, Giacomina Rossi, Martin Rossor, Elio Scarpini, David Tang-Wai, Hakan Thonberg, Pietro Tiraboschi, Ana Verdelho, and Jason Warren. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort. NeuroImage, 188:282-290, March 2019. Keyword(s): Clustering, Thalamus, Spatiotemporal geodesic regression, Parallel transport, Computational anatomy, Shape analysis. [bibtex-entry]



20. Raphaël Sivera, Hervé Delingette, Marco Lorenzi, Xavier Pennec, and Nicholas Ayache. A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments. NeuroImage, 198:255-270, September 2019. Keyword(s): Imaging biomarkers, Aging, Spatio-temporal model, Brain morphology, Deformations, Alzheimer's disease. [bibtex-entry]



21. Sebastiano Ferraris, Johannes van Der Merwe, Lennart van Der Veeken, Ferran Prados, Juan Eugenio Iglesias, Marco Lorenzi, Andrew Melbourne, Marc M Modat, Willy Gsell, Jan Deprest, and Tom Vercauteren. A magnetic resonance multi-atlas for the neonatal rabbit brain. NeuroImage, 179:187 - 198, October 2018. [bibtex-entry]



22. Marco Lorenzi, Andre Altmann, Boris Gutman, Selina Wray, Charles Arber, Derrek D Hibar, Neda J Jahanshad, Jonathan Schott, Daniel Alexander, Paul M. Thompson, and Sébastien Ourselin. Susceptibility of brain atrophy to TRIB3 in Alzheimer's disease, evidence from functional prioritization in imaging genetics. Proceedings of the National Academy of Sciences of the United States of America, 115(12):3162-3167, 2018. Keyword(s): bioinformatics, phenotype, imaging-genetics, Alzheimer's disease, genotype, GWA. [bibtex-entry]



23. Marzia A Scelzi, Raiyan R Khan, Marco Lorenzi, Christopher Leigh, Michael D Greicius, Jonathan M. Schott, Sébastien Ourselin, and Andre Altmann. Genetic study of multimodal imaging Alzheimer's disease progression score implicates novel loci. Brain - A Journal of Neurology, 141(7):2167 - 2180, July 2018. [bibtex-entry]



24. Marco Lorenzi, Maurizio Filippone, Giovanni Frisoni, Daniel C Alexander, and Sébastien Ourselin. Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer's disease. NeuroImage, October 2017. [bibtex-entry]



25. Alex F. Mendelson, Maria A. Zuluaga, Marco Lorenzi, Brian F. Hutton, and Sébastien Ourselin. Selection bias in the reported performances of AD classification pipelines. Neuroimage-Clinical, 14:400 - 416, 2017. Keyword(s): Alzheimer's disease, Classification, Cross validation, Selection bias, Overfitting, ADNI. [bibtex-entry]



26. Mehdi Hadj-Hamou, Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields. Frontiers in Neuroscience, 10(236):18, June 2016. Keyword(s): non-linear registration, deformation-based morphometry, longitudinal study, diffeomorphism parametrized by stationary velocity fields, statistical analysis, reproducible research. [bibtex-entry]



27. Bishesh Khanal, Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with Alzheimer's disease. NeuroImage, 134:35-52, July 2016. Keyword(s): longitudinal modeling, simulation of atrophy, longitudinal MRIs simulation, biophysical model, Alzheimer's disease. [bibtex-entry]



28. David M. Cash, Chris Frost, Leonardo O. Iheme, Devrim Ünay, Melek Kandemir, Jurgen Fripp, Olivier Salvado, Pierrick Bourgeat, Martin Reuter, Bruce Fischl, Marco Lorenzi, Giovanni B. Frisoni, Xavier Pennec, Ronald K. Pierson, Jeffrey L. Gunter, Matthew L. Senjem, Clifford R. Jack, Nicolas Guizard, Vladimir S. Fonov, D. Louis Collins, Marc Modat, Jorge M. Cardoso, Kelvin K. Leung, Hongzhi Wang, Sandhitsu R. Das, Paul A. Yushkevich, Ian B. Malone, Nick C. Fox, Jonathan M. Schott, and Sebastien Ourselin. Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge. NeuroImage, 123:149-164, December 2015. [bibtex-entry]



29. Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Regional flux analysis for discovering and quantifying anatomical changes: An application to the brain morphometry in Alzheimer's disease. NeuroImage, 115:224-234, July 2015. [bibtex-entry]



30. Marco Lorenzi, Xavier Pennec, Giovanni B. Frisoni, and Nicholas Ayache. Disentangling normal aging from Alzheimer's disease in structural magnetic resonance images. Neurobiology of Aging, 36:S42-S52, January 2015. [bibtex-entry]



31. Marco Lorenzi and Xavier Pennec. Discrete Ladders for Parallel Transport in Transformation Groups with an Affine Connection Structure. In Frank Nielsen, editor, Geometric Theory of Information, Signals and Communication Technology, pages 243-271. Springer, 2014. [bibtex-entry]



32. Marco Lorenzi, Nicholas Ayache, Giovanni B. Frisoni, and Xavier Pennec. LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage, 81(1):470-483, 2013. [bibtex-entry]



33. Marco Lorenzi and Xavier Pennec. Efficient Parallel Transport of Deformations in Time Series of Images: from Schild's to Pole Ladder. Journal of Mathematical Imaging and Vision, 50(1-2):5-17, October 2013. [bibtex-entry]



34. Marco Lorenzi and Xavier Pennec. Geodesics, Parallel Transport & One-parameter Subgroups for Diffeomorphic Image Registration. International Journal of Computer Vision, 105(2):111-127, November 2013. [bibtex-entry]



35. Roberta Rossi, Michela Pievani, Marco Lorenzi, Marina Boccardi, Rossella Beneduce, Stefano Bignotti, Genoveffa Borsci, Maria Cotelli, Panteleimon Giannakopoulos, Laura R. Magni, Luciana Rillosi, Sandra Rosini, Giuseppe Rossi, and Giovanni B. Frisoni. Structural brain features of borderline personality and bipolar disorders. Psychiatry Research: Neuroimaging, 12:S0925-4927, 2012. [bibtex-entry]



36. Marco Lorenzi, Alberto Beltramello, Nicola Mercuri, Elisa Canu, Giada Zoccatelli, Francesca Pizzini, Franco Alessandrini, Maria Cotelli, Sandra Rosini, Daniela Costardi, Carlo Caltagirone, and Giovanni Frisoni. Effect of memantine on resting state default mode network activity in Alzheimer's disease. Drugs and Aging, 28(3):205-217, 2011. [bibtex-entry]



37. Marco Lorenzi, Michael Donohue, Donata Paternico, Cristina Scarpazza, Susanne Ostrowitzki, Olivier Blin, Elaine Irving, and G. Frisoni. Enrichment through biomarkers in clinical trials of Alzheimer's drugs in patients with mild cognitive impairment. Neurobiology of Aging, pp 1443-51, August 2010. Note: Special Issue on ADNI. [bibtex-entry]

1. 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]



2. 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]



3. 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]



4. 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]



5. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. A General Theory for Client Sampling in Federated Learning. In International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22), Vienna, Austria, July 2022. [bibtex-entry]



6. Riccardo Taiello, Melek Önen, Olivier Humbert, and Marco Lorenzi. Privacy Preserving Image Registration. In MICCAI 2022 - Medical Image Computing and Computer Assisted Intervention, Singapore, Singapore, September 2022. Keyword(s): Image Registration, Privacy enhancing technologies, Trustworthiness. [bibtex-entry]



7. Jean Ogier Du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Telenczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, and Mathieu Andreux. FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. In NeurIPS 2022 - Thirty-sixth Conference on Neural Information Processing Systems, Proceedings of NeurIPS, New Orleans, United States, November 2022. [bibtex-entry]



8. Irene Balelli, Santiago Silva, and Marco Lorenzi. A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations. In International Conference on Information Processing in Medical Imaging, Information processing in medical imaging: proceedings of the 27th International Conference, IPMI 2021, Bornholm, Denmark, June 2021. Keyword(s): Federated Learning, Hierarchical Generative Model, Heterogeneity. [bibtex-entry]



9. Yann Fraboni, Richard Vidal, Laetitia Kameni, and Marco Lorenzi. Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. In ICML 2021 - 38th International Conference on Machine Learning, online, United States, July 2021. Keyword(s): Client sampling, federated learning, sampling variance, data representativity. [bibtex-entry]



10. Yann Fraboni, Richard Vidal, and Marco Lorenzi. Free-rider Attacks on Model Aggregation in Federated Learning. In AISTATS 2021 - 24th International Conference on Artificial Intelligence and Statistics, San Diego, United States, April 2021. [bibtex-entry]



11. Josquin Harrison, Marco Lorenzi, Benoit Legghe, Xavier Iriart, Hubert Cochet, and Maxime Sermesant. Phase-independent Latent Representation for Cardiac Shape Analysis. In MICCAI 2021 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, volume 12906 of LNCS - Lecture Notes in Computer Science, Strasbourg, France, September 2021. Keyword(s): Shape Analysis, Atrial Fibrillation, Thrombosis, Graph Representation, Latent Space Model, Multi-Task Learning, Meta-Learning. [bibtex-entry]



12. 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. [bibtex-entry]



13. 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, pages p.108-116, October 2020. Keyword(s): Post-infarct cardiac remodeling, Ventricular arrhythmia, Cross-sectional data, Disease progression modeling. [bibtex-entry]



14. 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. [bibtex-entry]



15. Luigi Antelmi, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. In ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, June 2019. [bibtex-entry]



16. Jaume Banus, Marco Lorenzi, Oscar Camara, and Maxime Sermesant. Large Scale Cardiovascular Model Personalisation for Mechanistic Analysis of Heart and Brain Interactions. In FIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart, Bordeaux, France, pages 285-293, June 2019. [bibtex-entry]



17. Sara Garbarino and Marco Lorenzi. Modeling and inference of spatio-temporal protein dynamics across brain networks. In IPMI 2019 - International Conference on Information Processing in Medical Imaging, volume 11492 of Lecture Notes in Computer Science book series, Hong Kong, Hong Kong SAR China, pages 57-69, June 2019. springer. Note: Bayesian non--parametric model, protein propagation, Alzheimer'sdisease, Gaussian process, dynamical systems, spatio--temporal model, disease progression modeling. [bibtex-entry]



18. Georgios Lazaridis, Marco Lorenzi, Sebastien Ourselin, and David Garway-Heath. Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Clinical Trials. In MICAI 2019 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, volume LNCS. LNIP - 11764 of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019, Shenzhen, China, pages 3-11, October 2019. Springer International Publishing. [bibtex-entry]



19. R azvan Marinescu, Marco Lorenzi, Stefano Blumberg, Alexandra Young, Pere Planell-Morell, Neil Oxtoby, Arman Eshaghi, Keir Yong, Sebastian Crutch, Polina Golland, and Daniel Alexander. Disease Knowledge Transfer Across Neurodegenerative Diseases. In MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, volume LNCS. LNIP - 11765 of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019, Shenzhen, China, pages 860-868, October 2019. Springer International Publishing. [bibtex-entry]



20. Clement Abi Nader, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes. In Machine Learning in Clinical Neuroimaging (MLCN) workshop, Granada, Spain, September 2018. Keyword(s): Alzheimer's Disease, Disease Progression Modelling, Gaussian Process. [bibtex-entry]



21. Luigi Antelmi, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease. In Understanding and Interpreting Machine Learning in Medical Image Computing Applications, volume 11038 of LNCS, Granada, Spain, pages 15-23, September 2018. [bibtex-entry]



22. Marco Lorenzi and Maurizio Filippone. Constraining the Dynamics of Deep Probabilistic Models. In ICML 2018 - The 35th International Conference on Machine Learning, volume 80 of PMLR - Proceedings of Machine Learning Research, Stockholm, Sweden, pages 3233-3242, July 2018. Note: 13 pages. Keyword(s): machine learning, dynamical systems, gradient matching, gaussian process. [bibtex-entry]



23. Santiago Silva, Boris A Gutman, Eduardo Romero, Paul M. Thompson, Andre Altmann, and Marco Lorenzi. Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data. In International Symposium on Biomedical Imaging, Venice, Italy, April 2018. [bibtex-entry]



24. R azvan Valentin Marinescu, Arman Esaghi, Marco Lorenzi, Alexandra L. Young, Neil P. Oxtoby, Sara Garbarino, Timothy J. Shakespeare, Sebastian Crutch, and Daniel C Alexander. A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images. In International Conference on Information Processing in Medical Imaging (IPMI), Boone, United States, July 2017. [bibtex-entry]



25. Bishesh Khanal, Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Simulating Patient Specific Multiple Time-point MRIs From a Biophysical Model of Brain Deformation in Alzheimer's Disease. In Workshop on Computational Biomechanics for Medicine - X, Computational Biomechanics for Medicine: Imaging, Modeling and Computing, Munich, France, pages 167-176, October 2015. Springer International Publishing. Keyword(s): biophysical modeling, Alzheimer's disease, biomechanical simulation, MRI, longitudinal MRI. [bibtex-entry]



26. Bishesh Khanal, Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. A Biophysical Model of Shape Changes due to Atrophy in the Brain with Alzheimer's Disease. In P. GOLLAND, N. HATA, C. BARILLOT, J. HORNEGGER, and R. HOWE, editors, MICCAI 2014 - 17th International Conference Medical Image Computing and Computer-Assisted Intervention, volume 8674 of LNCS - Lecture Notes in Computer Science, Springer, Boston, United States, pages 41-48, September 2014. Springer. Keyword(s): Alzheimer's disease, Biophysical model, Atrophy model, Atrophy simulation, Longitudinal modeling. [bibtex-entry]



27. Marco Lorenzi, Xavier Pennec, Nicholas Ayache, and (adni) For The Alzheimer'S Disease Neuroimaging Initiative. Regional flux analysis of longitudinal atrophy in Alzheimer's disease. In Alzheimer's Association International Conference, Copenhagen, Denmark, July 2014. Note: Oral podium presentation. [bibtex-entry]



28. Marco Lorenzi, Martina Bochetta, Nicholas Ayache, Xavier Pennec, and Giovanni B. Frisoni. Conversion to MCI in healthy individuals with abnormal CSF Ab42 levels is associated with specific longitudinal morphological changes. In Alzheimer's Association International Conference 2013, volume 9, issue 4 (supplement) of Alzheimer's & Dementia: The Journal of the Alzheimer's Association, Boston, United States, pages P596, July 2013. [bibtex-entry]



29. Marco Lorenzi, Bjoern H. Menze, Marc Niethammer, Nicholas Ayache, and Xavier Pennec. Sparse Scale-Space Decomposition of Volume Changes in Deformations Fields. In Kensaku Mori, Ichiro Sakuma, Yoshinobu Sato, Christian Barillot, and Nassir Navab, editors, Medical Image Computing and Computer Aided Intervention (MICCAI), volume 8150 of Lecture Notes in Computer Science - LNCS, Nagoya, Japan, pages 328-335, September 2013. Springer. Keyword(s): Scale-space decomposition, non-rigid registration, longitudinal analysis, stationary velocity fields. [bibtex-entry]



30. Marco Lorenzi and Xavier Pennec. Parallel Transport with Pole Ladder: Application to Deformations of time Series of Images. In Nielsen, Frank, Barbaresco, and F., editors, GSI2013 - Geometric Science of Information, volume 8085 of Lecture Notes in Computer Science - LNCS, Paris, France, pages 68-75, August 2013. Springer. [bibtex-entry]



31. Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Disentangling the normal aging from the pathological Alzheimer's disease progression on cross-sectional structural MR images. In MICCAI workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (NIBAD'12), pages 145-154, October 2012. ISBN: 9781479261994. [bibtex-entry]



32. Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Regional flux analysis of longitudinal atrophy in Alzheimer's disease. In Proceedings of Medical Image Computing and Computer Assisted Intervention 2012 (MICCAI), volume 7510 of LNCS, pages 739-746, October 2012. Springer, Heidelberg. ISBN: 978-3-642-33414-6. [bibtex-entry]



33. Marco Lorenzi, Giovanni B. Frisoni, Nicholas Ayache, and Xavier Pennec. Probabilistic Flux Analysis of Cerebral Longitudinal Atrophy. In MICCAI workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (NIBAD'12), pages 256-265, October 2012. ISBN: 9781479261994. [bibtex-entry]



34. Marco Lorenzi, Nicholas Ayache, Giovanni B. Frisoni, and Xavier Pennec. Mapping the effects of A$\beta_{1-42}$ levels on the longitudinal changes in healthy aging: hierarchical modeling based on stationary velocity fields. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 6892 of LNCS, pages 663-670, 2011. Springer. [bibtex-entry]



35. Marco Lorenzi, Nicholas Ayache, and Xavier Pennec. Schilds Ladder for the parallel transport of deformations in time series of images. In G. Szekely and H. Hahn, editors, Proceedings of Information Processing in Medical Imaging (IPMI'11), volume 6801 of LNCS, pages 463-474, 2011. Note: Honorable Mention (runner-up) for the Erbsmann Award. [bibtex-entry]



36. Marco Lorenzi and Xavier Pennec. Geodesics, parallel transport & one-parameter subgroups. In 3rd MICCAI workshop on Mathematical Foundations of Computational Anatomy Workshop, September 2011. [bibtex-entry]



37. Marco Lorenzi, Xavier Pennec, and Giovanni B. Frisoni. Monitoring the brain's longitudinal changes in clinical trials for Alzheimers disease: a robust and reliable non-rigid registration framework. In Alzheimer's Association 2011 International Conference on Alzheimer's Disease (AAICAD), 2011. [bibtex-entry]



38. Xavier Pennec and Marco Lorenzi. Which parallel transport for the statistical analysis of longitudinal deformations?. In Colloque GRETSI '11, September 2011. [bibtex-entry]



39. Marco Lorenzi, Nicholas Ayache, G. Frisoni, and Xavier Pennec. 4D registration of serial brain MR's images: a robust measure of changes applied to Alzheimer's disease. In Miccai Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data, Beijing, China, September 2010. Note: Best Oral Presentation award. [bibtex-entry]

1. Santiago Silva, Boris Gutman, Eduardo Romero, Paul M. Thompson, Andre Altmann, and Marco Lorenzi. Federated learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data (Supplementary Material). Research Report, Inria & Université Cote d'Azur, CNRS, I3S, Sophia Antipolis, France, October 2018. [bibtex-entry]

1. 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]



2. 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]



3. 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]



4. Luigi Antelmi, Nicholas Ayache, Philippe Robert, Federica Ribaldi, Valentina Garibotto, Giovanni B Frisoni, and Marco Lorenzi. Combining Multi-Task Learning and Multi-Channel Variational Auto-Encoders to Exploit Datasets with Missing Observations -Application to Multi-Modal Neuroimaging Studies in Dementia. Note: Working paper or preprint, May 2021. Keyword(s): Multi Task Learning, Missing Data, Variational Autoencoders, Multimodal Data Analysis, OPAL-Meso. [bibtex-entry]



5. Valeria Manera, Luigi Antelmi, Radia Zeghari, Nicholas Ayache, Marco Lorenzi, and Philippe Robert. Prevalence of lack of interest and anhedonia in the general population of the UK Biobank. AAIC 2019 - Alzheimer's Association International Conference, July 2019. Note: Poster. [bibtex-entry]



6. Radia Zeghari, Philippe Robert, Valeria Manera, Marco Lorenzi, and Alexandra König. Towards a Multidimensional Assessment of Apathy in Neurocognitive Disorders. AAIC 2019 - Alzheimer's Association International Conference, July 2019. Note: Poster. [bibtex-entry]



7. Clement Abi Nader, Nicholas Ayache, Valeria Manera, Philippe Robert, and Marco Lorenzi. Disentangling spatio-temporal patterns of brain changes in large-scale brain imaging databases through Independent Gaussian Process Analysis. 12ème Conférence Francophone d'Epidémiologie Clinique (EPICLIN) et 25èmes Journées des statisticiens des Centre de Lutte Contre le Cancer (CLCC), May 2018. Note: Poster. Keyword(s): Gaussian Process, Statistical Learning. [bibtex-entry]



8. Luigi Antelmi, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Supplementary Material of the paper: ''Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease'', July 2018. Note: Supplementary Material of the paper: ''Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease''. Paper accepted at the 1st International Workshop on Machine Learning in Clinical Neuroimaging, in conjunction with MICCAI 2018, September 20, Granada (Spain). [bibtex-entry]



9. Luigi Antelmi, Marco Lorenzi, Valeria Manera, Philippe Robert, and Nicholas Ayache. A method for statistical learning in large databases of heterogeneous imaging, cognitive and behavioral data.. EPICLIN 2018 - 12ème Conférence Francophone d'Epidémiologie Clinique / CLCC 2018 - 25èmes Journées des statisticiens des Centre de Lutte Contre le Cancer, May 2018. Note: Poster. Keyword(s): CCA, Statistical learning. [bibtex-entry]



10. Marco Lorenzi and Hélène Barelli. Blood Levels of Omega 3 and 6 across the Progression of Alzheimer's Disease. Alzheimer's Association International Conference (AAIC), 2018. Note: Poster. [bibtex-entry]



11. Clement Abi Nader, Nicholas Ayache, Philippe Robert, and Marco Lorenzi. Appendix Alzheimer's Disease Modelling and Staging through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes, July 2018. Note: Appendix. [bibtex-entry]



12. Claire Cury, Stanley Durrleman, David M. Cash, Marco Lorenzi, Jennifer M Nicholas, Martina Bocchetta, John Cornelis van Swieten, Barbara Borroni, Daniela Galimberti, Mario Masellis, Maria Carmela Tartaglia, James Rowe, Caroline Graff, Fabrizio Tagliavini, Giovanni B. Frisoni, Robert Laforce, Elizabeth Finger, Alexandre de Mendonça, Sandro Sorbi, Sébastien Ourselin, Jonathan D Rohrer, and Marc M Modat. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: applied to GENFI study. Note: Working paper or preprint, August 2018. Keyword(s): LDDMM, Frontotemporal Dementia, Shape Analysis, Genfi, Thalamus, Spatio temporal analysis, Morphometry. [bibtex-entry]



13. Marco Lorenzi, Nicholas Ayache, Xavier Pennec, Giovanni B. Frisoni, and for the Alzheimer's Disease Neuroimaging Initiative (ADNI). Differentiating pathological brain atrophy from normal aging: a promising diagnostic tool for Alzheimer's disease. 2nd Virtual Physiological Human European Conference (VPH2012), London, September 2012. [bibtex-entry]



14. Marco Lorenzi, Nicholas Ayache, Xavier Pennec, Giovanni B. Frisoni, and for the Alzheimer's Disease Neuroimaging Initiative (ADNI). Disentangling the normal aging from the pathological Alzheimer's disease progression on structural MR images. 5th Clinical Trials in Alzheimer's Disease (CTAD'12), Monte Carlo, October 2012. [bibtex-entry]



15. Marco Lorenzi, Michael Donohue, Donata Paternico, Cristina Scarpazza, Susanne Ostrowitzki, Olivier Blin, Elaine Irving, and G. Frisoni. Enrichment through biomarkers in clinical trials of Alzheimer's drugs in patients with mild cognitive impairment. Clinical Trials on Alzheimer's Disease International Conference, November 2010. [bibtex-entry]



16. Marco Lorenzi, G. Frisoni, Nicholas Ayache, and Xavier Pennec. Mapping longitudinal changes in the brain affected by Alzheimer's disease. First VPH Conference (VPH2010), Brussels), September 2010. [bibtex-entry]

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