Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
S. Jia.
Population-Based Models of Shape, Structure, and Deformation in Atrial Fibrillation, Université Côte d'Azur, December 2019.
https://hal.inria.fr/tel-02428638
[2]
P. Mlynarski.
Deep Learning for Segmentation of Brain Tumors and Organs at Risk in Radiotherapy Planning, Université Côte d'Azur, November 2019.
https://hal.inria.fr/tel-02358374
[3]
R. Sivera.
Modeling and measuring the brain morphological evolution using structural MRI in the context of neurodegenerative diseases, Université Cote d'Azur, November 2019.
https://hal.inria.fr/tel-02389924
[4]
Q. Zheng.
Deep Learning for Robust Segmentation and Explainable Analysis of 3D and Dynamic Cardiac Images, Inria - Sophia Antipolis, March 2019.
https://hal.inria.fr/tel-02083415

Articles in International Peer-Reviewed Journals

[5]
C. Abi Nader, N. Ayache, P. Robert, M. Lorenzi.
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data, in: NeuroImage, 2019.
https://hal.archives-ouvertes.fr/hal-02051843
[6]
L. Antelmi, N. Ayache, P. Robert, M. Lorenzi.
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data, in: Proceedings of Machine Learning Research, 2019, no 97, pp. 302–311.
https://hal.inria.fr/hal-02395747
[7]
N. Ayache, S. Colantonio.
The Digital Health Revolution - Introduction to the Special Theme, in: ERCIM News, July 2019, no 118, pp. 4-5.
https://hal.inria.fr/hal-02404520
[8]
P. Compes, E. Tabouret, A. Etcheverry, C. Colin, R. Appay, N. Cordier, J. Mosser, O. Chinot, H. Delingette, N. Girard, H. Dufour, P. Metellus, D. Figarella-Branger.
Neuro-radiological characteristics of adult diffuse grade II and III insular gliomas classified according to WHO 2016, in: Journal of Neuro-Oncology, 2019, vol. 142, no 3, pp. 511-520. [ DOI : 10.1007/s11060-019-03122-1 ]
https://hal-univ-rennes1.archives-ouvertes.fr/hal-02076599
[9]
C. Cury, S. Durrleman, D. Cash, M. Lorenzi, J. M. Nicholas, M. Bocchetta, J. C. Van Swieten, B. Borroni, D. Galimberti, M. Masellis, M. C. Tartaglia, J. Rowe, C. Graff, F. Tagliavini, G. B. Frisoni, R. Laforce, E. Finger, A. de Mendonça, S. Sorbi, S. Ourselin, J. Rohrer, M. Modat, C. Andersson, S. Archetti, A. Arighi, L. Benussi, S. Black, M. Cosseddu, M. Fallstrm, C. G. Ferreira, C. Fenoglio, N. Fox, M. Freedman, G. Fumagalli, S. Gazzina, R. Ghidoni, M. Grisoli, V. Jelic, L. Jiskoot, R. Keren, G. Lombardi, C. Maruta, L. Meeter, R. van Minkelen, B. Nacmias, L. Ijerstedt, A. Padovani, J. Panman, M. Pievani, C. Polito, E. Premi, S. Prioni, R. Rademakers, V. Redaelli, E. Rogaeva, G. Rossi, M. Rossor, E. Scarpini, D. Tang-Wai, H. Thonberg, P. Tiraboschi, A. Verdelho, J. Warren.
Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort, in: NeuroImage, March 2019, vol. 188, pp. 282-290. [ DOI : 10.1016/j.neuroimage.2018.11.063 ]
https://www.hal.inserm.fr/inserm-01958916
[10]
T. Demarcy, I. Pélisson, D. Gnansia, H. Delingette, N. Ayache, C. Raffaelli, C. Vandersteen, N. Guevara.
Un modèle de reconstruction tridimensionnelle de la cochlée au service de l’implantation cochléaire, in: Les cahiers de l'audition, July 2019, vol. 32, no 4, pp. 36-40, forthcoming.
https://hal.inria.fr/hal-02270604
[11]
R. Doste, D. Soto‐Iglesias, G. Bernardino, A. Alcaine, R. Sebastian, S. Giffard-Roisin, M. Sermesant, A. Berruezo, D. Sanchez‐Quintana, O. Camara.
A rule‐based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts, in: International Journal for Numerical Methods in Biomedical Engineering, March 2019, vol. 35, no 4. [ DOI : 10.1002/cnm.3185 ]
https://hal.inria.fr/hal-02128531
[12]
J. S. Duncan, M. F. Insana, N. Ayache.
Biomedical Imaging and Analysis In the Age of Big Data and Deep Learning, in: Proceedings of the IEEE, January 2020, vol. 14, pp. 3-10.
https://hal.archives-ouvertes.fr/hal-02395429
[13]
S. Heeke, J. Benzaquen, E. Long-Mira, B. Audelan, V. Lespinet, O. Bordone, S. Lalvée, K. Zahaf, M. Poudenx, O. Humbert, H. Montaudié, P.-M. Dugourd, M. Chassang, T. Passeron, H. Delingette, C.-H. Marquette, V. Hofman, A. Stenzinger, M. Ilié, P. Hofman.
In-House Implementation of Tumor Mutational Burden Testing to Predict Durable Clinical Benefit in Non-Small Cell Lung Cancer and Melanoma Patients, in: Cancers, 2019, vol. 11. [ DOI : 10.3390/cancers11091271 ]
https://hal.inria.fr/hal-02381188
[14]
S. Heeke, H. Delingette, Y. Fanjat, E. Long-Mira, S. Lassalle, V. Hofman, J. Benzaquen, C.-H. Marquette, P. Hofman, M. Ilié.
La pathologie cancéreuse pulmonaire à l’heure de l’intelligence artificielle : entre espoir, désespoir et perspectives, in: Annales de Pathologie, April 2019, vol. 39, no 2, pp. 130-136. [ DOI : 10.1016/j.annpat.2019.01.003 ]
https://hal.inria.fr/hal-02446712
[15]
P. Hofman, N. Ayache, P. Barbry, M. Barlaud, A. Bel, P. Blancou, F. Checler, S. Chevillard, G. Cristofari, M. Demory, V. Esnault, C. Falandry, E. Gilson, O. Guerin, N. Glaichenhaus, J. Guigay, M. Ilié, B. Mari, C.-H. Marquette, V. Paquis-Flucklinger, F. Prate, P. Saintigny, B. Seitz-Polsky, T. Skhiri, E. Van Obberghen-Schilling, E. V. Obberghen, L. Yvan-Charvet.
The OncoAge Consortium: Linking Aging and Oncology from Bench to Bedside and Back Again, in: Cancers, February 2019. [ DOI : 10.3390/cancers11020250 ]
https://hal.inria.fr/hal-02045442
[16]
J. Krebs, H. Delingette, B. Mailhé, N. Ayache, T. Mansi.
Learning a Probabilistic Model for Diffeomorphic Registration, in: IEEE Transactions on Medical Imaging, February 2019, pp. 2165-2176, https://arxiv.org/abs/1812.07460. [ DOI : 10.1109/TMI.2019.2897112 ]
https://hal.archives-ouvertes.fr/hal-01978339
[17]
P. Mlynarski, H. Delingette, A. Criminisi, N. Ayache.
Deep Learning with Mixed Supervision for Brain Tumor Segmentation, in: Journal of Medical Imaging, July 2019, https://arxiv.org/abs/1812.04571. [ DOI : 10.1117/1.JMI.6.3.034002 ]
https://hal.inria.fr/hal-01952458
[18]
P. Mlynarski, H. Delingette, A. Criminisi, N. Ayache.
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context, in: Computerized Medical Imaging and Graphics, February 2019, vol. 73, pp. 60-72, https://arxiv.org/abs/1807.08599, forthcoming. [ DOI : 10.1016/j.compmedimag.2019.02.001 ]
https://hal.inria.fr/hal-01883716
[19]
F. Orlhac, C. Bouveyron, N. Ayache.
Radiomics: How to Make Medical Images Speak?, in: ERCIM News, July 2019, no 118, pp. 7-8.
https://hal.inria.fr/hal-02404530
[20]
F. Orlhac, F. Frouin, C. Nioche, N. Ayache, I. Buvat.
Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics, in: Radiology, April 2019, vol. 291, no 1, pp. 53-59. [ DOI : 10.1148/radiol.2019182023 ]
https://hal.archives-ouvertes.fr/hal-02401340
[21]
M. Sermesant.
Improving Cardiac Arrhythmia Therapy with Medical Imaging, in: ERCIM News, July 2019, no 118, pp. 10-11.
https://hal.inria.fr/hal-02404534
[22]
R. Sivera, N. Capet, V. Manera, R. Fabre, M. Lorenzi, H. Delingette, X. Pennec, N. Ayache, P. Robert.
Voxel based assessments of treatment effects on longitudinal brain changes in the MAPT cohort, in: Neurobiology of Aging, 2019, forthcoming.
https://hal.inria.fr/hal-02166357
[23]
R. Sivera, H. Delingette, M. Lorenzi, X. Pennec, N. Ayache.
A model of brain morphological changes related to aging and Alzheimer’s disease from cross-sectional assessments, in: NeuroImage, September 2019, vol. 198, pp. 255-270, https://arxiv.org/abs/1905.09826. [ DOI : 10.1016/j.neuroimage.2019.05.040 ]
https://hal.inria.fr/hal-01948174
[24]
M. Takigawa, J. Duchateau, F. Sacher, R. Martin, K. Vlachos, T. Kitamura, M. Sermesant, N. Cedilnik, G. Cheniti, A. Frontera, N. Thompson, C. Martin, G. Massoullié, F. Bourier, A. Lam, M. Wolf, W. Escande, C. André, T. Pambrun, A. Denis, N. Derval, M. Hocini, M. Haïssaguerre, H. Cochet, P. Jaïs.
Are wall thickness channels defined by computed tomography predictive of isthmuses of postinfarction ventricular tachycardia?, in: Heart Rhythm, June 2019, vol. 16, no 11, pp. 1661-1668. [ DOI : 10.1016/j.hrthm.2019.06.012 ]
https://hal.inria.fr/hal-02181776
[25]
W. Wei, E. Poirion, B. Bodini, S. Durrleman, N. Ayache, B. Stankoff, O. Colliot.
Predicting PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for Multiple Sclerosis, in: Medical Image Analysis, 2019, forthcoming. [ DOI : 10.1016/j.media.2019.101546 ]
https://hal.archives-ouvertes.fr/hal-02276634
[26]
W. Wei, E. Poirion, B. Bodini, S. Durrleman, O. Colliot, B. Stankoff, N. Ayache.
Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis, in: Journal of Medical Imaging, February 2019, vol. 6, no 01. [ DOI : 10.1117/1.JMI.6.1.014005 ]
https://hal.inria.fr/hal-02042526
[27]
W. Wimmer, L. Anschuetz, S. Weder, F. Wagner, H. Delingette, M. Caversaccio.
Human bony labyrinth dataset: Co-registered CT and micro-CT images, surface models and anatomical landmarks, in: Data in Brief, December 2019, vol. 27, 104782 p. [ DOI : 10.1016/j.dib.2019.104782 ]
https://hal.inria.fr/hal-02402404
[28]
Q. Zheng, H. Delingette, N. Ayache.
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow, in: Medical Image Analysis, 2019, vol. 56, pp. 80-95, https://arxiv.org/abs/1811.03433, forthcoming. [ DOI : 10.1016/j.media.2019.06.001 ]
https://hal.inria.fr/hal-01975880

International Conferences with Proceedings

[29]
L. Antelmi, N. Ayache, P. Robert, M. 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.
https://hal.inria.fr/hal-02154181
[30]
B. Audelan, H. Delingette.
Unsupervised Quality Control of Image Segmentation based on Bayesian Learning, in: MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China, October 2019, pp. 21-29.
https://hal.inria.fr/hal-02265131
[31]
N. Ayache.
AI and Healthcare: towards a Digital Twin?, in: MCA 2019 - 5th International Symposium on Multidiscplinary Computational Anatomy, Fukuoka, Japan, March 2019.
https://hal.inria.fr/hal-02063234
[32]
I. Ayed, N. Cedilnik, P. Gallinari, M. Sermesant.
EP-Net: Learning Cardiac Electrophysiology Models for Physiology-based Constraints in Data-Driven Predictions, in: FIMH 2019 - 10th International Conference on Functional Imaging of the Hearth, Bordeaux, France, Springer, June 2019, pp. 55-63.
https://hal.inria.fr/hal-02106618
[33]
T. Bacoyannis, J. Krebs, N. Cedilnik, H. Cochet, M. Sermesant.
Deep Learning Formulation of ECGI for Data-driven Integration of Spatiotemporal Correlations and Imaging Information, in: FIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart, Bordeaux, France, Springer, June 2019, vol. LNCS 11504, pp. 20-28.
https://hal.inria.fr/hal-02108958
[34]
J. Banus, M. Lorenzi, O. Camara, M. 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, May 2019, pp. 285-293. [ DOI : 10.1007/978-3-030-21949-9_31 ]
https://hal.inria.fr/hal-02361466
[35]
N. Cedilnik, J. Duchateau, F. Sacher, P. Jaïs, H. Cochet, M. Sermesant.
Fully Automated Electrophysiological Model Personalisation Framework from CT Imaging, in: FIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart, Bordeaux, France, June 2019, pp. 325-333.
https://hal.inria.fr/hal-02106609
[36]
N. Cedilnik, S. Jia, P. Jaïs, H. Cochet, M. Sermesant.
Automatic non-invasive substrate analysis from CT images in post-infarction VT, in: EHRA 2019 - European Heart Rhythm Association, Lisbonne, Portugal, March 2019, vol. 21, no 2, pp. 720-739. [ DOI : 10.1093/europace/euz105 ]
https://hal.inria.fr/hal-02181793
[37]
N. Cedilnik, M. Sermesant.
Eikonal Model Personalisation using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response, in: STACOM 2019 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Shenzen, China, October 2019.
https://hal.inria.fr/hal-02368288
[38]
G. Desrues, H. Delingette, M. Sermesant.
Towards Hyper-Reduction of Cardiac Models using Poly-Affine Deformation, in: STACOM 2019: Statistical Atlases and Computational Models of the Heart, Shenzhen, China, October 2019.
https://hal.inria.fr/hal-02429678
[39]
N. Guigui, S. Jia, M. Sermesant, X. Pennec.
Symmetric Algorithmic Components for Shape Analysis with Diffeomorphisms, in: GSI 2019 - 4th conference on Geometric Science of Information, Toulouse, France, Geometric Science of Information, August 2019, vol. 11712, 10 p, https://arxiv.org/abs/1906.05921.
https://hal.inria.fr/hal-02148832
[40]
Best Paper
J. Krebs, T. Mansi, N. Ayache, H. Delingette.
Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI, in: STACOM 2019 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Shenzhen, China, October 2019, https://arxiv.org/abs/1907.13524 - Probabilistic Motion Model, Motion Tracking, Temporal Super-Resolution, Diffeomorphic Registration, Temporal Variational Autoencoder.
https://hal.inria.fr/hal-02239318
[41]
B. Ly, H. Cochet, M. Sermesant.
Style Data Augmentation for Robust Segmentation of Multi-Modality Cardiac MRI, in: Statistical Atlases and Computational Modelling of the Heart, Shenzhen, China, October 2019.
https://hal.inria.fr/hal-02401643
[42]
Y. Thanwerdas, X. Pennec.
Exploration of Balanced Metrics on Symmetric Positive Definite Matrices, in: GSI 2019 - 4th conference on Geometric Science of Information, Toulouse, France, Proceedings of Geometric Science of Information, August 2019, pp. 484-493, https://arxiv.org/abs/1909.03852.
https://hal.archives-ouvertes.fr/hal-02158525
[43]
Y. Thanwerdas, X. Pennec.
Is affine invariance well defined on SPD matrices? A principled continuum of metrics, in: GSI 2019 - 4th conference on Geometric Science of Information, Toulouse, France, Proceedings of Geometric Science of Information, August 2019, pp. 502-510, https://arxiv.org/abs/1906.01349.
https://hal.archives-ouvertes.fr/hal-02147020
[44]
Z. Wang, C. Vandersteen, T. Demarcy, D. Gnansia, C. Raffaelli, N. Guevara, H. Delingette.
Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging, in: MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China, October 2019, pp. 121-129.
https://hal.inria.fr/hal-02196557
[45]
W. Wimmer, C. Vandersteen, N. Guevara, M. Caversaccio, H. Delingette.
Robust Cochlear Modiolar Axis Detection in CT, in: MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, October 2019, pp. 3-10. [ DOI : 10.1007/978-3-030-32254-0_1 ]
https://hal.inria.fr/hal-02402475
[46]
Y. Yang, S. Gillon, J. Banus, P. Moceri, M. Sermesant.
Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?, in: STACOM 2019 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Shenzhen, China, October 2019.
https://hal.inria.fr/hal-02382941

Conferences without Proceedings

[47]
B. Chakraborty, S. Giffard-Roisin, M. Alessandrini, B. Heyde, M. Sermesant, J. D'Hooge.
Estimation of the Spatial Resolution of a 2D Strain Estimator Using Synthetic Cardiac Images, in: IUS 2018 - IEEE International Ultrasonics Symposium, Kobe, Japan, IEEE, February 2019, pp. 1-9. [ DOI : 10.1002/cnm.3185 ]
https://hal.archives-ouvertes.fr/hal-02024010
[48]
Best Paper
S. Garbarino, M. Lorenzi.
Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks, in: IPMI 2019 - 26th International Conference on Information Processing in Medical Imaging, Hong-Kong, China, LNCS, Springer, 2019, vol. 11492, pp. 57-69.
https://hal.inria.fr/hal-02165021
[49]
P. Moceri, N. Duchateau, D. Baudouy, C. Sanfiorenzo, F. Squara, E. Ferrari, M. Sermesant.
Incremental prognostic value of changes in 3D right ventricular function in pulmonary hypertension, in: JE SFC 2019 - 29es Journées Européennes de la Société Française de Cardiologie, Paris, France, January 2019.
https://hal.archives-ouvertes.fr/hal-02161692
[50]
P. Moceri, N. Duchateau, D. Baudouy, F. Squara, E. Ferrari, M. Sermesant.
3D right ventricular strain and shape in volume overload: comparative analysis of Tetralogy of Fallot and atrial septal defect patients, in: JE SFC 2019 - 29es Journées Européennes de la Société Française de Cardiologie, Paris, France, January 2019.
https://hal.archives-ouvertes.fr/hal-02161694
[51]
P. Moceri, N. Duchateau, N. Dursent, X. Iriart, S. Hascoet, D. Baudouy, E. Ferrari, M. Sermesant.
Right ventricular remodelling in CHD-PAH patients using 3D speckle tracking, in: 30es Journées Européennes de la Société Française de Cardiologie, Paris, France, Archives of Cardiovascular Diseases Supplements, 2020, vol. 12, pp. 163-4.
https://hal.archives-ouvertes.fr/hal-02445303

Scientific Books (or Scientific Book chapters)

[52]
N. Miolane, L. Devilliers, X. Pennec.
Bias on estimation in quotient space and correction methods, in: Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, September 2019, no Chap. 9, pp. 343-376. [ DOI : 10.1016/B978-0-12-814725-2.00017-0 ]
https://hal.inria.fr/hal-02342155
[53]
X. Pennec, S. Sommer, T. Fletcher.
Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, September 2019. [ DOI : 10.1016/C2017-0-01561-6 ]
https://hal.inria.fr/hal-02341896
[54]
X. Pennec, M. Lorenzi.
Beyond Riemannian geometry: The affine connection setting for transformation groups, in: Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, September 2019, no Chap. 5, pp. 169-229. [ DOI : 10.1016/B978-0-12-814725-2.00012-1 ]
https://hal.inria.fr/hal-02342137
[55]
X. Pennec.
Manifold-valued image processing with SPD matrices, in: Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, September 2019, no Chap. 3, pp. 75-134. [ DOI : 10.1016/B978-0-12-814725-2.00010-8 ]
https://hal.inria.fr/hal-02341958
[56]
S. Sommer, T. Fletcher, X. Pennec.
Introduction to differential and Riemannian geometry, in: Riemannian Geometric Statistics in Medical Image Analysis, Elsevier, September 2019, no Chap. 1, pp. 3-37. [ DOI : 10.1016/b978-0-12-814725-2.00008-x ]
https://hal.inria.fr/hal-02341901

Books or Proceedings Editing

[57]
D. Zhu, J. Yan, S. Durrleman, S. Sommer, H. Huang, L. Shen, P. M. Thompson, C.-F. Westin, X. Pennec, S. Joshi, M. Nielsen, T. Fletcher (editors)
Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy : 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings, Lecture Notes in Computer Science (LNCS), Springer, October 2019, vol. 11846. [ DOI : 10.1007/978-3-030-33226-6 ]
https://hal.inria.fr/hal-02341877

Patents

[58]
J. Krebs, H. Delingette, N. Ayache, T. Mansi, S. Miao.
Medical Imaging Diffeomorphic Registration based on Machine Learning, July 2019, no US 2019/0205766 A1.
https://hal.inria.fr/hal-02185900

Other Publications

[59]
V. Manera, L. Antelmi, R. Zeghari, N. Ayache, M. Lorenzi, P. Robert.
Prevalence of lack of interest and anhedonia in the general population of the UK Biobank, July 2019, AAIC 2019 - Alzheimer’s Association International Conference, Poster.
https://hal.inria.fr/hal-02174565
[60]
N. Miolane, J. Mathe, C. Donnat, M. Jorda, X. Pennec.
geomstats: a Python Package for Riemannian Geometry in Machine Learning, January 2019, https://arxiv.org/abs/1805.08308 - Preprint NIPS2018.
https://hal.inria.fr/hal-01974572
[61]
P. Mlynarski, H. Delingette, H. Alghamdi, P.-Y. Bondiau, N. Ayache.
Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks, July 2019, https://arxiv.org/abs/1907.02003 - In the revision process:.
https://hal.inria.fr/hal-02181181
[62]
X. Pennec.
Curvature effects on the empirical mean in Riemannian and affine Manifolds: a non-asymptotic high concentration expansion in the small-sample regime, June 2019, https://arxiv.org/abs/1906.07418 - working paper or preprint.
https://hal.inria.fr/hal-02157952
[63]
R. Zeghari, P. Robert, V. Manera, M. Lorenzi, A. König.
Towards a Multidimensional Assessment of Apathy in Neurocognitive Disorders, July 2019, vol. 15, no 7, 569 p, AAIC 2019 - Alzheimer's Association International Conference, Poster. [ DOI : 10.1016/j.jalz.2019.06.4514 ]
https://hal.archives-ouvertes.fr/hal-02339152
[64]
Q. Zheng, H. Delingette, K. Fung, S. E. Petersen, N. Ayache.
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank, February 2019, https://arxiv.org/abs/1902.05811 - working paper or preprint.
https://hal.inria.fr/hal-02043380