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 Publications: 

Articles in Journals:

• 37 - Development and clinical validation of a real-time AI diagnostic companion for fetal ultrasound examination. J. J. Stirnemann, R. Besson, E. Spaggiari, S. Rojo, F. Loge, H. Peyro-Saint-Paul, S. Allassonniere, E. Le Pennec, Y. Ville. Ultrasound in Obstetrics and Gynecology journal, 2023 Sep;62(3):353-360. doi: 10.1002/uog.26242.

• 36 - Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

C. Chadebec, E. Thibeau-Sutre, N. Burgos, and S. Allassonnière. IEEE Pattern Analysis and Machine Intelligence  

• 35 - Spatio-temporal mixture process estimation to detect population dynamical changes. S. Pruilh, A.S. Jannot, S. Allassonnière. Artif Intell Med (2022).

• 34 - Le profil de méthylome du sang total comme biomarqueur de l’excès des glucocorticoïdes R. Armignacco et al. Annales d'Endocrinologie, Volume 82(5)

• 33 - VP17. 02: Description and clinical validation of a real‐time AI diagnostic companion for fetal ultrasound examination. J Stirnemann, R Besson, E Spaggiari, N Bourgon, S Rojo, F Loge, H Peyro‐Saint‐Paul, S Allassonniere, E Le Pennec, Y Ville. Ultrasound in Obstetrics & Gynecology, Volume 58

• 32 - Identification of glucocorticoid-related molecular signature by whole blood methylome analysis. R. Armignacco et al. European Journal of Endocrinology, Volume 186 (2), Pages 297-308

• 31 - On the curved exponential family in the Stochatic Approximation Expectation Maximization Algorithm. V. Debavelaere and S. Allassonnière. ESAIM: Probability and Statistics, Volume 25, 2021.

• 30 - Mixture of Conditional Gaussian Graphical Models for Unlabelled Heterogeneous Populations in the Presence of Co-factors. T. Lartigue, S. Durrleman, S. Allassonnière. SN Computer Science, 466 (2021).

• 29 - Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold. C. Mantoux, B. Couvy-Duchesne, F. Cacciamani, S. Epelbaum, S. Durrleman, S. Allassonnière. Accepted for publication in Entropy (2021)

• 28 - AD Course Map charts Alzheimer’s disease progression. I. Koval, A. Bône, M. Louis, T. Lartigue, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonnière and S. Durrleman. Scientific Report 2021.

• 27 - On the convergence of stochastic approximations under a subgeometric ergodic Markov dynamic. V. Debavelaere, S. Durrleman and S. Allassonnière. Electronic Journal of Statistics 2021, Vol. 15, No. 1, 1583-1609. 

• 26 - A new class of EM algorithms. Escaping local maxima and handling intractable sampling. S.Allassonnière and J. Chevallier. Computational Statistics and Data Analysis, 2021. https://doi.org/10.1016/j.csda.2020.107159

• 25 - A coherent framework for learning spatiotemporal piecewise-geodesic trajectories from longitudinal malifold-valued data. J. Chevallier, V. Debavelaere and S. Allassonnière. To appear in SIAM Journal en Imaging Sciences, 2021.

• 24 - Learning the clustering of longitudinal shape data sets into a mixture of independent or branching trajectories. V Debavelaere, S Durrleman, S Allassonnière. International Journal of Computer Vision. (2020)

• 23 - Gaussian Graphical Model exploration and selection in high dimension low sample size setting. T. Lartigue, S. Bottani, S. Baron, O. Colliot, S. Durrleman, S. Allassonnière for the Alzheimer’s Disease Neuroimaging Initiative. IEEE Transactions on Pattern Analysis and Machine Intelligence. (2020)

• 22 - Learning from both experts and data. R Besson, E Le Pennec, S Allassonnière. Entropy 21 (12), 1208 (2019)

• 21- Spatiotemporal propagation of the cortical atrophy during the course of Alzheimer’s Disease : Population and individual patterns”, Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stephanie Allassonniere and Stanley Durrleman, Frontiers in Neurology-Neurodegeneration. 2018.

• 20- A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations. J.B. Schiratti, S. Allassonniere, O. Colliot ans S. Durrleman. Journal of Machine Learning Research, 2017,  vol 18(133) (pdf).

• 19- Inconsistency of Template Estimation by Minimizing of the Variance/Pre-Variance in the Quotient Space. L. Devilliers, S. Allassonnière, A. Trouvé, X. Pennec. Entropy 2017, 19(6), 288; doi:10.3390/e19060288

• 18- Template estimation in computational anatomy: Fréchet means top and quotient spaces are not consistent, L. Devilliers, S. Allassonnière, A. Trouvé, X. Pennec. SIAM J. Imaging Sci. 10-3 (2017), pp1139-1169 (2017).

• 17- Phase-based Metamorphosis of Diffusion Lesion in Relation to Perfusion Values in Acute Ischemic Stroke. I. Rekik, S. Allassonniere, M. Luby, T. Carpenter and J. Wardlaw. NeuroImage Clinical 9(C):44-49, 2015. DOI: 10.1016/j.nicl.2015.07.007.

• 16- Bayesian Mixed Effect Atlas Estimation under Diffeomorphic constraint on the deformation model. S.Allassonnière, S.Durrleman and E.Kuhn. SIAM Journal on Imaging Science., 8(3), 1367–1395, 2015.  (pdf)

• 15- Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation. S. Allassonniere and E. Kuhn. Journal of Computational Statistics & Data Analysis, Volume 91(C), 2015. 

• 14- Using longitudinal metamorphosis to examine ischemic stroke lesion dynamics on perfusion- weighted and relation to final outcome on T2-w images. I. Rekik, S. Allassonniere, T. Carpenter and J. Wardlaw. NeuroImage Clinical, 5, 2014, pp 332-340. 

• 13- Bayesian estimation of Probabilisitic atlas for tissue segmentation. H. Xu, B.Thirion and S. Allassonniere, IRBM Journal, 35(1), 2014, pp 27-32 (pdf).

• 12- Probabilistic Atlas and Geometric Variability Estimation to Drive Tissue Segmentation. H. Xu, B.Thirion and S. Allassonniere, Statistics in Medicine, 33(20), 2014, pp 3576–3599. 

• 11- Statistical models for deformable templates in image and shape analysis. S. Allassonniere, J. Bigot, J.A. Glaunes, F. Maire and F.J.P. Richard. Annales mathématiques Blaise Pascal, vol 20(1), 2013.

• 10- Spacio-temporal dynamic simulation of acute perfusion/diffusion ischemic stroke lesions evolution: a pilot study derived from longitudinal MR patient data. I. Rekik, S. Allassonniere, S. Durrleman, T. Carpenter and J. Wardlaw. Computational and Mathematical Methods in Medicine, vol 2013, doi:10.1155/2013/283593, 2013 (pdf).

• 9- A Review of Medical Image Analysis Methods in MR/CT-imaged Acute-subacute Ischemic Stroke Lesion: Segmentation, Prediction and Insights into Dynamic Evolution Simulation Models. I. Rekik, S. Allassonniere, T. Carpenter and J. Wardlaw. NeuroImage Clinical Volume 1, Issue 1, (2012), (164-178) (pdf).

• 8- Sparse Adaptive Parameterization of Variability in Image Ensembles. S. Durrleman, S. Allassonniere, S.Joshi. IJCV, 2012, (DOI) 10.1007/s11263-012-0556-1.

• 7- Aircraft classification with a low resolution infrared sensor. S. Lefebvre, S. Allassonniere, J. Jakubowicz, T. Lasne, and E. Moulines. Machine Vision and Application Journal, 2013, Vol. 24(1), pp 175-186 (pdf).

• 6- Tumor Growth Parameters Estimation and Source Localization From a Unique Time Point: Application to Low-grade Gliomas. I. Rekik, S. Allassonniere, H. Deslingette, O. Clatz, E. Geremia, N. Ayache.  Computer Vision and Image Understanding, 117(3), 2013, pp 238-249.

• 5- A Stochastic Algorithm for Probabilistic Independent Component Analysis. S. Allassonnière, L. Younes.  Annals of Applied Statistics, 2012, Vol. 6, No. 1, 125-160 (.pdf)

• 4- Bayesian Consistent Estimation in Deformable Models using Stochastic Algorithms: Applications to Medical Images. S. Allassonnière, E. Kuhn, A. Trouvé. Journal de la Société Française de Statistiques. Vol. 151(1) p. 1-16, 2010.

• 3- Stochastic Algorithm For Parameter Estimation For Dense Deformable Template Mixture Model. S. Allassonnière, E. Kuhn.  ESAIM-PS 2010, Vol 14, p. 382_408. (.pdf)

• 2- Construction of Bayesian deformable models via stochastic approximation algorithm: A convergence study.  S. Allassonnière, E. Kuhn, A. Trouvé. Bernoulli Journal, Vol 16(3), p.641-678, 2010. (pdf)

• 1- Toward a coherent statistical framework for dense deformable template estimation. S. Allassonnière, Y. Amit, A. Trouvé. Volume 69, part 1 (2007), pages 3-29, of the Journal of the Royal Statistical Society, Series B.  (pdf)

Patents

• A METHOD FOR DETERMINING THE TEMPORAL PROGRESSION OF A BIOLOGICAL PHENOMENON AND ASSOCIATED METHODS AND DEVICES. S Durrleman, J Schiratti, S Allassonniere, O Colliot. US Patent App. 16/300,391

• Real-time diagnosis aid method and decision-support system for medical diagnosis to a user of a medical system » S. Allassonnière, R. Besson, E. Le Pennec. Patent App. 16/692 137

• METHOD IMPLEMENTED BY COMPUTER MEANS FOR CHARACTERIZING AT LEAST ONE OBSERVATION OF A SUBJECT. BOEKEN Tom, DEBAVELAERE Vianney, FEYDY Jean,  ALLASSONNIERE Stéphanie, PELLERIN Olivier, and SAPOVAL Marc.  (Pattent application)

PhD and HDR dissertations:

• HDR manuscript: Statistical analysis of Deformable Template Models for medical image understanding. (pdf)

• PhD Dissertation: Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy. S.Allassonnière. (pdf)

Conference Articles:

1. 1. A multiscale algorithm for computing realistic image transformations – Application to the modelling of fetal brain growth. Fleur Gaudfernau, Stéphanie Allassonniere, Erwan Le Pennec. SPIE Medical Imaging 2023

   2. Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case. Chadebec, C., Vincent, L. J. and Allassonnière, S. Neural Information Processing Systems (NeurIPS 2022) Dataset and Benchmark.

   3. A Geometric Perspective on Variational Autoencoders. Chadebec, C. and Allassonnière, S. Neural Information Processing Systems (NeurIPS 2022).

   4. An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories. Chadebec, C., Huijben, E. M., Pluim, J. P., Allassonnière, S. and J.M van Eijnatten, M. A.. DGM4MICCAI: MICCAI Workshop on Deep Generative Models (2022)

   5. Robust Reinforcement Learning with Distributional Risk-averse formulation. P.Clavier, S. Allassonnière, E. Le Pennec. RDMDE 2022

   6. A spatio-temporal atlas of the foetal brain with agenesis of the corpus callosum. F. Gaudfernau, A. O'Keane, E. Blondieau, S. Allassonnière. FIT’NG Conference 2022

   7. Data Augmentation with Variational Autoencoders and Manifold Sampling. C. Chadebec, S. Allassonnière. Proceedings of the 2021 MICCAI DALI workshop

   8. Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis. F. Gaudfernau, E. Blondiaux, S. Allassonnière. Proceedings of the PIPPI 2021, MICCAI workshop 

   9. Analyse statistique de données anatomiques longitudinales de patients traités. Application au suivi de chimiothérapie. J. Chevallier, S. Allassonnière.  Proceedings of the "52èmes Journées de Statistiques de la Société Française de Statistique" (2020)

   10. Optimisation des parcours patients pour lutter contre l’errance de diagnostic des patients atteints de maladies rares.  F. Logé, R. Besson, S. Allassonnière. Proceedings of the "52èmes Journées de Statistiques de la Société Française de Statistique" (2020)

   11. A decision support tool for the diagnosis of rare diseases. R. Besson, E. Le Pennec, E. Spaggiari, A. Neuraz, J. Stirnemann and S.Allassonnière. ICAART (2020).

   12. Clustering of longitudinal shape data sets using mixture of separate or branching trajectories. V Debavelaere, A Bône, S Durrleman, S Allassonnière. International Conference on Medical Image Computing and Computer-Assisted Intervention. (2019)

   13. Deciphering the progression of PET alterations using surface-based spatiotemporal modeling. Igor Koval, Arnaud Marcoux, Ninon Burgos, Stéphanie Allassonnière, Olivier Colliot, et Stanley Durrleman. OHBM 2019 - Annual meeting of the Organization for Human Brain Mapping, (2019), Rome, Italy

   14. Optimization of a Sequential Decision Making Problem for a Disease Diagnostic Application. Rémi Besson, Erwan Le Pennec, Julien Stirnemann, Stéphanie Allassonnière. R/Medicine 2018

   15. Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data. NIPS 2017. Juliette Chevallier · Stephane Oudard and Stéphanie Allassonniere

   16. Statistical Learning of Spatiotemporal Patterns from Longitudinal Manifold-Valued Networks. I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière, S. Durrleman. MICCAI 2017

   17. Optimisation d'arbre de décision pour un problème de détection précoce d'anomalies foetales. R. Besson, E. Le Pennec, S. Allassonnière. JDS2017.

   18. Inconsistency of Template Estimation with the Fréchet mean in Quotient Space. Loïc Devilliers, Xavier Pennec, and Stéphanie Allassonnière. In Information Processing in Medical Imaging 2017, Boone, United States, June 2017.

   19. Estimating the Template in the Total Space with the Fréchet Mean on Quotient Spaces may have a Bias: a Case Study on Vector Spaces Quotiented by the Group of Translations. S. Allassonnière, L. Devilliers, X. Pennec. Proceedings of the fifth international workshop on Mathematical Foundation sof Computational Anatomy (MFCA'15), Munich, Germany, pages 131-142, 2015

   20. Learning spatiotemporal trajectories from manifold-valued longitudinal data. J.B Schiratti, S. Allassonnière, O. Colliot, S. Durrleman. NIPS 2015.

   21. Innovation-based sparse estimation of functional connectivity from multivariate autoregressive models. F. Deloche, F. De Vico Fallani, S. Allassonnière. Proc. SPIE 9597, Wavelets and Sparsity XVI, 95971I (August 24, 2015); doi:10.1117/12.2189640; Conference Volume 9597

   22.  Mixed-effects model for the spatiotemporal analysis of longitudinal manifold-valued data. J-B.  Schiratti, S. Allassonnière, O. Colliot, S. Durrleman. MFCA 2015

   23. Estimating the Template in the Total Space with the Fréchet Mean on Quotient Spaces may have a Bias: a Case Study on Vector Spaces Quotiented by the Group of Translations.  S. Allassonnière, L. Devilliers, X. Pennec. MFCA 2015.

   24. A mixed effect model with time reparametrization for longitudinal univariate manifold valued data. J.B. Schiratti, S. Allassonnière, O. Colliot, S. Durrleman. Information Processing in Medical Imaging (IPMI) 2015

   25. Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses. H. Xu, B. Thirion and S. Allassonniere. MICCAI, Nagoya, 2013

   26. A 4D Patient-specific metamorphosis-based method to model ischemic stroke lesion evo- lution from acute diffusion-weighted to final T2-defined outcome. I. Rekik, S. Allassonnière, T. Carpenter and J. Wardlaw. International Stroke Conference, Honolulu, 2013. 

   27. 4D spatio-temporal perfusion-diffusion Evolution scenarios estimation in Acute-subacute ischemic stroke. I. Rekik, S. Allassonniere, S. Durrleman, T.K. Carpenter, J.M. Wardlaw, ISMRM, Melbroune, 2012.

   28. Dynamic Patient-specific Modeling of Ischemic Stroke Lesion Evolution: From Presentation to Final Damage using Diffusion, Perfusion and T2 MR imaging, I. Rekik, S. Allassonniere, S. Durrleman, T.K. Carpenter, J.M. Wardlaw, ESC, Lisbon, 2012.

   29. Mathematical Image Processing Algorithms in determining Stroke Tissue Status and Pre- dicting its Fate: Systematic Review reveals Untapped Potentials. I. Rekik, S. Allassonnière, T. Carpenter and J. Wardlaw.. European Stroke Conference, Hamburg 2011. 

   30. Detection and classification of poorly known aircraft with a low-resolution infrared sensor. S. Lefebvre, S. Allassonnière, G. Durand, J. Jakubowicz, E. Moulines, A. Roblin. Proc. of SPIE volume 8050 (2011).

   31. Detecting long distance conditional correlations between anatomical regions using Gaussian Graphical Models. S. Allassonnière, P.Jolivet, C.Giraud. Mathematical Fundation of Computational Anatomy (MFCA) workshop of the MICCAI 2011 conference. (pdf)

   32. Consistent Atlas Estimation on BME Template Model: Applications to 3D Biomedical Images. S. Allassonnière, E. Kuhn, J. T. Ratnanather, A. Trouvé. Probabilistic Models for Medical Image Analysis (PMMIA) worshop of the MICCAI 2009 conference. (pdf)

   33. MAP Estimation of Statistical Deformable Template Via Nonlinear Mixed Effect Models~:

   34. Deterministic and Stochastic Approaches. S.Allassonnière, E.Kuhn, A.Trouvé. Mathematical Foundations of Computational Anatomy (MFCA) workshop of MICCAI 2008 conference. (pdf)

   35. Generative Model and consistent estimation algorithms for non-rigid deformable models. S.Allassonnière, E.Kuhn, Y.Amit, A.Trouvé. ICASSP conference, 2006 in Toulouse, France  (pdf)

   36. Geodesic Shooting and Diffeomorphic Matching Via Textured Meshes. S.Allassonnière, A.Trouvé, L.Younes. EMMCVPR 2005, St Augustine, Florida, USA. (pdf)

Several Presentations and Seminars (to be completed):

• Seminar at the Institut National de Recherche Agronomique (INRA)  in Toulouse (November 2013)

• Seminar at the University Paris Dauphine (November 2013)

• Seminar at the CIMI thematic trimester on image processing (May 2013)

• Presentation of the AMALA sampler at the conference Méthodes Mathématiques pour l'Image (Orléans, June 2012)

• Presentation of the AMALA sampler at the BigMC seminar (January 2012)

• Mini-course on  Computational Anatomy at the Forum des jeunes mathématiciennes (November 2011)

• Seminar at the Institut des mathématiques in Toulouse for the Statistics and Imaging meeting (June 2011)

• Presentation of current work at the annual Shape meeting, Imperial College London (May 2011)

• Tutorial on Computational Anatomy at the KTH university in Stockholm (October 2010)

• Image lunch seminar at the Scientific Computing and Imaging Institute (University of Utah September 2010)

• Organization of the mini-symposium on Statistics for medical Image Analysis, part of the IMA conference on Imaging Science (April 2010)

• Poster presentation at the  Probabilistic Models for Medical Image Analysis (PMMIA) worshop of the MICCAI 2009 conference. (pmmiabis.pdf)

• Presentation at the Summer School Mathematics in Brain Imaging   in the Institute of Pure and Applied Mathematics (UCLA)  Generative Models and Stochastic Algorithms for Population Average Estimation and Image Analysis."(07/15/08).

•  Seminar at the Applied Mathematics and Statistics department in Johns Hopkins University (03/04/08) :  Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy.

• Seminar Siemens Corporate Research (04/02/08) :  Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy.

• Seminar in University of Maryland Baltimore County (UMBC),  Department of Mathematics and Statistics (16/11/07) :  Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy.

• Presentation in the New Researcher Session of the SAMSI Summer 2007 Program on the Geometry and Statistics of Shape Spaces (July 7-13, 2007) : Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy

• Thesis defense, University Paris 13 (07/04/07)  : Representation and Statistical Estimation of Deformable Template Models for Shape Recognition and Computational Anatomy.

• Seminar in LATP University of Provense, Marseille (12/01/06)  :  Toward a coherent statistical framework for dense deformable template estimation.

• Presentation in the colloquim of Centre de Mathematiques et leurs Applications, Cachan, France, (05/18/06)  : Toward a coherent statistical framework for dense deformable template estimation.

• Presentation in the ICASSP conference, Toulouse (05/16/06) : Generative Model and consistent estimation algorithms for non-rigid deformable models.

• Poster session in the workshop in IMA on Shape Spaces (02-06/04/06) : Generative Model and consistent estimation algorithms for non-rigid deformation model.

• Presentation in the ENST for the "groupe de travail" ISIS and MSPC (09/06/05) : Geodesic Shooting and Diffeomorphic Matching Via Textured Meshes.

• Presentation in the LAGA probability and statistics seminar (13/04/05): Large Deformations and Triangulation for Image Matching Problems.

• Presentation in the Center for Imaging Science in Johns Hopkins University (Baltimore) (15/03/05): Large Deformations and Triangulation for Image Matching Problems. 

Conferences: 

• MICCAI 2009. London. Sept 20th to Sept 24th.

• MICCAI 2008. New York City. Sept 6th to Sept 10th.

• Summer School in UCLA: Mathematics in Brain Imaging (2008)

• SAMSI Summer 2007 Program on the Geometry and Statistics of Shape Spaces (July 7-13, 2007)

• Workshop in Paris Dauphine University on Mathematics and Image Analysis ( 2004 and 2006)

• IMA workshop on Shape Spaces (Apr 2006)

• International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006)

• Energy Minimisation Methods for Computer Vision and Pattern Recognition (EMMCVPR 2005)  

• Summer School in UCLA: Mathematics in Brain Imaging (2004)