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Diana MATEUS
ENSEIGNANT-CHERCHEUR
Professeur des universitésPublications référencées sur HAL

Revues internationales avec comité de lecture (ART_INT)
- [1] A. Jiménez-Sánchez, M. Tardy, M. González Ballester, D. Mateus, G. Piella. Memory-aware curriculum federated learning for breast cancer classification. In Computer Methods and Programs in Biomedicine ; éd. Elsevier, 2023, vol. 229.https://hal.science/hal-04024535
- [2] A. Jiménez-Sánchez, D. Mateus, S. Kirchhoff, C. Kirchhoff, P. Biberthaler, N. Navab, G. Piella, M. González Ballester. Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty. In Medical Image Analysis ; éd. Elsevier, 2022, vol. 75.https://hal.science/hal-03431434
- [3] M. Tardy, D. Mateus. Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms. In Frontiers in Radiology, vol. 1. 11-01-2022https://hal.science/hal-03606170
- [4] M. Millardet, S. Moussaoui, J. Idier, D. Mateus, M. Conti, C. Bailly, S. Stute, T. Carlier. A Multiobjective Comparative Analysis of Reconstruction Algorithms in the Context of Low-Statistics 90 Y-PET Imaging. In IEEE Transactions on Radiation and Plasma Medical Sciences ; éd. IEEE, 2022, vol. 6, num. 6.https://hal.science/hal-03961422
- [5] C. Fourcade, L. Ferrer, N. Moreau, G. Santini, A. Brennan, C. Rousseau, M. Lacombe, V. Fleury, M. Colombié, P. Jézéquel, M. Campone, M. Rubeaux, D. Mateus. Deformable image registration with deep network priors: a study on longitudinal PET images. In Physics in Medicine and Biology ; éd. IOP Publishing, 2022, vol. 67, num. 15.https://hal.science/hal-03584128v2
- [6] D. Al Chanti, V. Gonzalez Duque, M. Crouzier, A. Nordez, L. Lacourpaille, D. Mateus. IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound. In IEEE Transactions on Medical Imaging ; éd. Institute of Electrical and Electronics Engineers, 2021.https://hal.science/hal-03197457
- [7] B. Jamet, L. Morvan, C. Nanni, A. Michaud, C. Bailly, S. Chauvie, P. Moreau, C. Touzeau, E. Zamagni, C. Bodet-Milin, F. Kraeber-Bodéré, D. Mateus, T. Carlier. Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials. In European Journal of Nuclear Medicine and Molecular Imaging ; éd. Springer Verlag (Germany), 2021, vol. 48, num. 4.https://inserm.hal.science/inserm-03498841
- [8] M. Tardy, D. Mateus. Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction. In IEEE Transactions on Medical Imaging ; éd. Institute of Electrical and Electronics Engineers, 2021, vol. 40, num. 10.https://hal.science/hal-03606162
- [9] M. Millardet, S. Moussaoui, D. Mateus, J. Idier, T. Carlier. Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to 90 Y-PET. In IEEE Transactions on Medical Imaging ; éd. Institute of Electrical and Electronics Engineers, 2020, vol. 39.https://hal.science/hal-02565204
- [10] A. Jiménez-Sánchez, A. Kazi, S. Albarqouni, C. Kirchhoff, P. Biberthaler, N. Navab, S. Kirchhoff, D. Mateus. Precise proximal femur fracture classification for interactive training and surgical planning. In International Journal of Computer Assisted Radiology and Surgery ; éd. Springer Verlag, 2020.https://hal.science/hal-02564696
- [11] L. Morvan, T. Carlier, B. Jamet, C. Bailly, C. Bodet-Milin, P. Moreau, F. Kraeber-Bodere, D. Mateus. Leveraging RSF and PET images for prognosis of Multiple Myeloma at diagnosis. In International Journal of Computer Assisted Radiology and Surgery ; éd. Springer Verlag, 2019.https://hal.science/hal-02172435
- [12] J. Renner, H. Phlipsen, B. Haller, F. Navarro-Avila, Y. Saint-Hill-Febles, D. Mateus, T. Ponchon, A. Poszler, M. Abdelhafez, R. Schmid, S. von Delius, P. Klare. Optical classification of neoplastic colorectal polyps – a computer-assisted approach (the COACH study). In Scandinavian Journal of Gastroenterology ; éd. Taylor & Francis, 2018, vol. 53, num. 9.https://hal.science/hal-02049344
- [13] L. Peter, D. Mateus, P. Chatelain, D. Declara, N. Schworm, S. Stangl, G. Multhoff, N. Navab. Assisting the examination of large histopathological slides with adaptive forests. In Medical Image Analysis ; éd. Elsevier, 2017, vol. 35.https://inria.hal.science/hal-01695986
- [14] B. Gutiérrez-Becker, D. Mateus, L. Peter, N. Navab. Guiding multimodal registration with learned optimization updates. In Medical Image Analysis ; éd. Elsevier, 2017, vol. 41.https://inria.hal.science/hal-01695990
- [15] J. Perez-Gonzalez, F. Arámbula-Cosío, M. Guzmán, L. Camargo, B. Gutierrez, D. Mateus, N. Navab, V. Medina-Bañuelos. Spatial Compounding of 3-D Fetal Brain Ultrasound Using Probabilistic Maps. In Ultrasound in Medicine & Biology ; éd. Elsevier, 2017, vol. 44, num. 1.https://inria.hal.science/hal-01695992
- [16] F. Cuzzolin, D. Mateus, R. Horaud. Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies. In International Journal of Computer Vision ; éd. Springer Verlag, 2015, vol. 112, num. 1.https://hal.science/hal-01053737
- [17] D. Volpi, M. Sarhan, R. Ghotbi, N. Navab, D. Mateus, S. Demirci. Online tracking of interventional devices for endovascular aortic repair. In International Journal of Computer Assisted Radiology and Surgery ; éd. Springer Verlag, 2015, vol. 10, num. 6.https://inria.hal.science/hal-01695949
- [18] V. Castaneda, D. Mateus, N. Navab. Stereo Time-of-Flight with Constructive Interference. In IEEE Transactions on Pattern Analysis and Machine Intelligence ; éd. Institute of Electrical and Electronics Engineers, 2014, vol. 36, num. 7.https://inria.hal.science/hal-01694247
- [19] D. Mateus, L. Schwarz, N. Navab. Recognizing multiple human activities and tracking full-body pose in unconstrained environments. In Pattern Recognition ; éd. Elsevier, 2012, vol. 45, num. 1.https://inria.hal.science/hal-01690285
- [20] L. Schwarz, A. Mkhitaryan, D. Mateus, N. Navab. Human skeleton tracking from depth data using geodesic distances and optical flow. In Image and Vision Computing ; éd. Elsevier, 2012, vol. 30, num. 3.https://inria.hal.science/hal-01692292
- [21] S. Atasoy, D. Mateus, A. Meining, G. Yang, N. Navab. Endoscopic Video Manifolds for Targeted Optical Biopsy. In IEEE Transactions on Medical Imaging ; éd. Institute of Electrical and Electronics Engineers, 2012, vol. 31, num. 3.https://inria.hal.science/hal-01693178
- [22] A. Aswathi, M. Rizkallah, G. Frecon, C. Bailly, C. Bodet-Milin, O. Casasnovas, S. Le Gouill, F. Kraeber-Bodéré, T. Carlier, D. Mateus. Lesion graph neural networks for 2-year progression free survival classification of Diffuse Large B-Cell Lymphoma patients. In International Symposium on Biomedical Imaging, avril 2023, Cartagena de Indias, Colombie.https://hal.science/hal-03975221
- [23] H. Carrillo Lindado, M. Millardet, T. Carlier, D. Mateus. Low-count PET image reconstruction with Bayesian inference over a Deep Prior. In Image Processing, février 2021, Online Only, états-Unis.https://inserm.hal.science/inserm-03546657
- [24] M. Tardy, D. Mateus. Trainable Summarization to Improve Breast Tomosynthesis Classification. In International Conference on Medical Image Computing and Computer Assisted Intervention, septembre 2021, Strasbourg, France.https://hal.science/hal-03606195
- [25] D. Al Chanti, D. Mateus. OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation. In the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, septembre 2021, Strasbourg (virtuel), France.https://hal.science/hal-03261428
- [26] G. Pelluet, M. Rizkallah, O. Acosta, D. Mateus. Unsupervised Multimodal Supervoxel Merging towards Brain Tumor Segmentation. In BrainLes 2021 MICCAI workshop, septembre 2021, Strasbourg, France.https://hal.science/hal-03561699
- [27] M. Tardy, D. Mateus. Lightweight U-Net for high-resolution breast imaging. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), avril 2020, Iowa City, états-Unis.https://hal.science/hal-02565347
- [28] A. Jiménez-Sánchez, A. Kazi, S. Albarqouni, C. Kirchhoff, P. Biberthaler, N. Navab, S. Kirchhoff, D. Mateus. Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning. In International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), juin 2020, Munich, Allemagne.https://hal.science/hal-02564707
- [29] M. Tardy, D. Mateus. Improving Mammography Malignancy Segmentation by Designing the Training Process. In Medical Imaging with Deep Learning, juillet 2020, Montreal, Canada.https://hal.science/hal-02566358
- [30] C. Fourcade, L. Ferrer, G. Santini, N. Moreau, C. Rousseau, M. Lacombe, C. Guillerminet, M. Colombié, M. Campone, D. Mateus, M. Rubeaux. Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images *. In EMBC - Engineering in Medecine and Biology Conference, juillet 2020, Montréal, Canada.https://hal.science/hal-02565092
- [31] V. Gonzalez Duque, D. Al Chanti, M. Crouzier, A. Nordez, L. Lacourpaille, D. Mateus. Spatio-temporal Consistency and Negative Label Transfer for 3D freehand US Segmentation. In the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention,, octobre 2020, Lima, Pérou.https://hal.science/hal-02734902
- [32] M. Tardy, B. Scheffer, D. Mateus. Breast Density Quantification Using Weakly Annotated Dataset. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), avril 2019, Venice, Italie.https://hal.science/hal-02463086
- [33] L. Morvan, T. Carlier, C. Bailly, B. Jamet, C. Bodet-Milin, P. Moreau, C. Touzeau, F. Kraeber-Bodere, D. Mateus. Leveraging Random Survival Forest (RSF) and PET images for prognosis of Multiple Myeloma at diagnosis. In International Conference on Information Processing in Computer-Assisted Interventions (IPCAI), juin 2019, Rennes, France.https://hal.science/hal-02174921
- [34] M. Tardy, B. Scheffer, D. Mateus. A closer look onto breast density with weakly supervised dense-tissue masks. In CARS 2019—Computer Assisted Radiology and Surgery Proceedings of the 33rd International Congress and Exhibition, Rennes, France, June 18–21, 2019, juin 2019, Rennes, France.https://hal.science/hal-02565263
- [35] M. Tardy, B. Scheffer, D. Mateus. A closer look onto breast density with weakly supervised dense-tissue masks. In Medical Imaging with Deep Learning MIDL 2019, juillet 2019, London, Royaume-Uni.https://hal.science/hal-02463094
- [36] A. Jiménez-Sánchez, D. Mateus, S. Kirchhoff, C. Kirchhoff, P. Biberthaler, N. Navab, M. González Ballester, G. Piella. Medical-based Deep Curriculum Learning for Improved Fracture Classification. In International Conference on Medical Image Computing and Computer Aided Interventions, octobre 2019, Shenzen, Chine.https://hal.science/hal-02458516
- [37] M. Tardy, B. Scheffer, D. Mateus. Uncertainty Measurements for the Reliable Classification of Mammograms. In International Conference on Medical Image Computing and Computer Assisted Intervention, octobre 2019, Shenzen, Chine.https://hal.science/hal-02463111
- [38] A. Jiménez-Sánchez, S. Albarqouni, D. Mateus. Capsule Networks against Medical Imaging Data Challenges. In MICCAI Workshop LABELS (Large-Scale Annotation of Biomedical Data and Expert Label Synthesis), septembre 2018, Granada, Espagne.https://hal.science/hal-02049352
- [39] M. Millardet, S. Moussaoui, D. Mateus, J. Idier, M. Conti, T. Carlier. A comparative study of AML, NEGML and OSEM based on experimental and clinical 90Y-PET data using the CASToR platform. In Medical Imaging Conference, novembre 2018, Sydney, Australie.https://hal.science/hal-01953013
- [40] F. Navarro-Avila, Y. Saint-Hill-Febles, J. Renner, P. Klare, S. von Delius, N. Navab, D. Mateus. Computer assisted optical biopsy for colorectal polyps. In SPIE Medical Imaging, mars 2017, Orlando, états-Unis.https://inria.hal.science/hal-01695984
- [41] B. Gutiérrez-Becker, D. Mateus, L. Peter, N. Navab. Learning Optimization Updates for Multimodal Registration. In International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), octobre 2016, Athenes, Grèce.https://inria.hal.science/hal-01695963
- [42] J. Perez-Gonzalez, F. Arámbula Cosío, M. Guzman, L. Camargo, B. Gutierrez, D. Mateus, N. Navab, V. Medina-Bañuelos. Ultrasound Fetal Brain Registration Using Weighted Coherent Point Drift. In International Symposium on Medical Information Processing and Analysis, décembre 2016, Tandil, Argentine.https://inria.hal.science/hal-01695979
- [43] M. Simonovsky, B. Gutiérrez-Becker, D. Mateus, N. Navab, N. Komodakis. A Deep Metric for Multimodal Registration. In 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), octobre 2016, Athènes, Grèce.In Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells (éds.), . Springer, 2016.https://hal.science/hal-01576914
- [44] C. Hennersperger, M. Baust, D. Mateus, N. Navab. Computational Sonography. In International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), octobre 2015, Munich, Allemagne.https://inria.hal.science/hal-01695952
- [45] M. Zweng, P. Fallavolita, S. Demirci, M. Kowarschik, N. Navab, D. Mateus. Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising. In AE-CAI Workshop at the International Conference on Medical Image Computing and Computer Aided Interventions, octobre 2015, Munich, Allemagne.https://inria.hal.science/hal-01695947
- [46] L. Peter, O. Pauly, P. Chatelain, D. Mateus, N. Navab. Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, octobre 2015, Munich, Allemagne.In Nassir Navab (éds.), . Springer International Publishing, 2015.https://inria.hal.science/hal-01241978
- [47] B. Gutierrez, D. Mateus, E. Shiban, B. Meyer, J. Lehmberg, N. Navab. A sparse approach to build shape models with routine clinical data. In 11th International Symposium on Biomedical Imaging (ISBI), avril 2014, Beijing, Chine.https://inria.hal.science/hal-01693187
- [48] N. Rieke, C. Hennersperger, D. Mateus, N. Navab. Ultrasound interactive segmentation with tensor-graph methods. In 11th International Symposium on Biomedical Imaging (ISBI), avril 2014, Beijing, Chine.https://inria.hal.science/hal-01694226
- [49] L. Peter, D. Mateus, P. Chatelain, N. Schworn, S. Stangl, G. Multhoff, N. Navab. Leveraging Random Forests for Interactive Exploration of Large Histological Images. In Int. Conf. on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, septembre 2014, Boston, états-Unis.https://inria.hal.science/hal-01056993
- [50] C. Hennersperger, D. Mateus, M. Baust, N. Navab. A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data.. In International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), septembre 2014, Boston, états-Unis.https://inria.hal.science/hal-01694235
- [51] Y. Chen, T. Hrabe, S. Pfeffer, O. Pauly, D. Mateus, N. Navab, F. Forster. Detection and identification of macromolecular complexes in cryo-electron tomograms using support vector machines. In 9th International Symposium on Biomedical Imaging (ISBI), mai 2012, Barcelona, Espagne.https://inria.hal.science/hal-01692295
- [52] S. Atasoy, D. Mateus, A. Meining, G. Yang, N. Navab. Targeted optical biopsies for surveillance endoscopies.. In International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), octobre 2012, Nice, France.https://inria.hal.science/hal-01693180
- [53] T. Birdal, D. Mateus, S. Ilic. Towards A Complete Framework For Deformable Surface Recovery Using RGBD Cameras. In International Robots and Systems (IRoS), Workshop on Color-Depth Fusion in Robotics, octobre 2012, Vila Moura, Portugal.https://inria.hal.science/hal-01692294
- [54] L. Schwarz, J. Lallemand, D. Mateus, N. Navab. Tracking planes with Time of Flight cameras and J-linkage. In Workshop on Applications of Computer Vision (WACV), janvier 2011, Kona, états-Unis.https://inria.hal.science/hal-01690331
- [55] V. Castaneda, D. Mateus, N. Navab. SLAM combining ToF and high-resolution cameras. In 2011 IEEE Workshop on Applications of Computer Vision (WACV), janvier 2011, Kona, états-Unis.https://inria.hal.science/hal-01690320
- [56] A. Safi, V. Castaneda, T. Lasser, D. Mateus, N. Navab. Manifold learning for dimensionality reduction and clustering of skin spectroscopy data. In SPIE Medical Imaging, mars 2011, Lake Buena Vista, états-Unis.https://inria.hal.science/hal-01690329
- [57] O. Pauly, B. Glocker, A. Criminisi, D. Mateus, A. Möller, S. Nekolla, N. Navab. Fast multiple organ detection and localization in whole-body MR dixon sequences.. In International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), septembre 2011, Toronto, Canada.https://inria.hal.science/hal-01690326
- [58] O. Pauly, D. Mateus, N. Navab. Building Implicit Dictionaries based on Extreme Random Clustering for Modality Recognition. In Medical Content-Based Retrieval for Clinical Decision Support MCBR-CDS 2011, septembre 2011, Toronto, Canada.https://inria.hal.science/hal-01690325
- [59] D. Mateus, V. Castaneda, N. Navab. Stereo time-of-flight. In IEEE International Conference on Computer Vision (ICCV), novembre 2011, Barcelona, Espagne.https://inria.hal.science/hal-01694248
- [60] D. Mateus, C. Wachinger, A. Keil, N. Navab. Manifold learning for patient position detection in MRI. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, avril 2010, Rotterdam, France.https://inria.hal.science/hal-01690309
- [61] L. Schwarz, D. Mateus, N. Navab. Multiple-Activity Human Body Tracking in Unconstrained Environments. In Articulated Motion and Deformable Objects, 6th International Conference, AMDO 2010, juillet 2010, Port d'Andratx, Espagne.https://inria.hal.science/hal-01690293
- [62] L. Schwarz, D. Mateus, V. Castaneda, N. Navab. Manifold Learning for ToF-based Human Body Tracking and Activity Recognition. In British Machine Vision Conference (BMVC) 2010, août 2010, Aberystwyth, Royaume-Uni.https://inria.hal.science/hal-01690306
- [63] S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G. Yang, N. Navab. Endoscopic video manifolds.. In International Conference in Medical Imaging and Computer Aided Interventions (MICCAI), septembre 2010, Beijing, Chine.https://inria.hal.science/hal-01690318
- [64] D. Mateus, S. Atasoy, A. Georgiou, N. Navab, G. Yang. Wave Interference for Pattern Description. In ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II Pages 41-54, novembre 2010, Queensland, Nouvelle-Zélande.https://inria.hal.science/hal-01690301
- [65] A. Bronstein, M. Bronstein, U. Castellani, A. Dubrovina, L. Guibas, R. Horaud, R. Kimmel, D. Knossow, E. von Lavante, D. Mateus, M. Ovsjanikov, A. Sharma. SHREC'10 track: correspondence finding. In 3DOR2010 - Eurographics Workshop on 3D Object Retrieval, mai 2010, Norrköping, Suède.In Mohamed Daoudi and Tobias Schreck and Michela Spagnuolo and Ioannis Pratikakis and Remco C. Veltkamp and Theoharis Theoharis (éds.), . Eurographics Association, 2010.https://inria.hal.science/inria-00590262
- [66] S. Atasoy, B. Glocker, S. Giannarou, D. Mateus, A. Meining, G. Yang, N. Navab. Probabilistic region matching in narrow-band endoscopy for targeted optical biopsy.. In International Conference on Medical Image Computing and Computer Assisted Intervention, septembre 2009, Londres, Royaume-Uni.https://inria.hal.science/hal-01689590
- [67] N. Padoy, D. Mateus, D. Weinland, M. Berger, N. Navab. Workflow Monitoring based on 3D Motion Features. In Workshop on Video-Oriented Object and Event Classification in Conjunction with ICCV 2009, septembre 2009, Kyoto, Japon.https://inria.hal.science/inria-00429355
- [68] L. Schwarz, D. Mateus, N. Navab. Discriminative Human Full-Body Pose Estimation from Wearable Inertial Sensor Data. In Modelling the Physiological Human. 3DPH 2009., novembre 2009, Zermatt, Suisse.https://inria.hal.science/hal-01689321
- [69] D. Knossow, A. Sharma, D. Mateus, R. Horaud. Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors. In 7th International Workshop on Graph-Based Representations in Pattern Recognition, mai 2009, Venice, Italie.In Andrea Torsello and Francisco Escolano and Luc Brun (éds.), Graph-Based Representations in Pattern Recognition. Springer, 2009.https://inria.hal.science/inria-00446989
- [70] D. Mateus, R. Horaud, D. Knossow, F. Cuzzolin, E. Boyer. Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration. In CVPR 2008 - IEEE Conference on Computer Vision and Pattern Recognition, juin 2008, Anchorage, états-Unis.https://inria.hal.science/inria-00590251
- [71] F. Cuzzolin, D. Mateus, D. Knossow, E. Boyer, R. Horaud. Coherent Laplacian 3-D Protrusion Segmentation. In CVPR 2008 - IEEE Conference on Computer Vision and Pattern Recognition, juin 2008, Anchorage, états-Unis.https://inria.hal.science/inria-00590250
- [72] D. Mateus, R. Horaud. Spectral Methods for 3-D Motion Segmentation of Sparse Scene-Flow. In WMVC 2007 - IEEE Workshop on Motion and Video Computing, février 2007, Austin, états-Unis.https://inria.hal.science/inria-00590241
- [73] D. Mateus, F. Cuzzolin, R. Horaud, E. Boyer. Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment. In NRTL 2007 - Workshop on Non-rigid Registration and Tracking through Learning, octobre 2007, Rio de Janeiro, Brésil.https://inria.hal.science/inria-00590237
- [74] D. Mateus, F. Cuzzolin, R. Horaud, E. Boyer. Articulated Shape Matching by Robust Alignment of Embedded Representations. In 3DRR 2007 - IEEE Workshop on 3D Representation for Recognition, octobre 2007, Rio de Janeiro, Brésil.https://inria.hal.science/inria-00590238
- [75] F. Cuzzolin, D. Mateus, E. Boyer, R. Horaud. Robust Spectral 3D-bodypart Segmentation along Time. In 2nd Workshop on Human Motion, Understanding, Modeling, Capture and Animation, octobre 2007, Rio de Janeiro, Brésil.In Ahmed Elgammal and Bodo Rosenhahn and Reinhard Klette (éds.), . Springer-Verlag, 2007.https://inria.hal.science/inria-00590229
- [76] F. Devernay, D. Mateus, M. Guilbert. Multi-Camera Scene Flow by Tracking 3-D Points and Surfels. In International Conference on Computer Vision and Pattern Recognition, 2006, New York, états-Unis.https://inria.hal.science/inria-00262285
- [77] D. Mateus, J. Avina Cervantes, M. Devy. Robot Visual Navigation in Semi-structured Outdoor Environments. In 2005 IEEE International Conference on Robotics and Automation, avril 2005, Barcelona, Espagne.https://inria.hal.science/hal-01689316
- [78] C. Fourcade, G. Santini, L. Ferrer, C. Rousseau, M. Colombié, M. Campone, M. Rubeaux, D. Mateus. Active Organs Segmentation in Metastatic Breast Cancer Images combining Superpixels and Deep Learning Methods. In NTHS - Nuclear Technology for Health Symposium, février 2020, Nantes, France.https://hal.science/hal-02565107
- [79] L. Morvan, T. Carlier, D. Mateus. The Limitations of Deep Learning: A Focus on Survival Analysis with PET Images.. In 4th Nuclear Technologies for Health Symposium (NTHS 2020), février 2020, Nantes, France.https://hal.science/hal-02569261
- [80] L. Morvan, D. Mateus, C. Bailly, B. Jamet, C. Bode-Milin, P. Moreau, C. Touzeau, F. Kraeber-Bodéré, T. Carlier. Prédiction de la progression chez des patients atteints de myélome multiple par "Random Survival Forest. In SFMN, mars 2019, Paris, France.https://hal.science/hal-02175296
- [81] L. Morvan, C. Nanni, A. Michaud, B. Jamet, C. Bailly, C. Bodet-Milin, S. Chauvie, C. Touzeau, P. Moreau, E. Zamagni, F. Kraeber-Bodéré, T. Carlier, D. Mateus. Learned Deep Radiomics for Survival Analysis with Attention. In Predictive Intelligence in Medicine (PRIME) 2020, Held in Conjunction with MICCAI 2020, Lima, Peru. 01-10-2020 https://hal.science/hal-03266299
- [82] A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton, D. Mateus, G. Martinez Möller, S. Nekolla, N. Navab. Anatomy Detection and Localization in 3D Medical Images. In Decision Forests for Computer Vision and Medical Image Analysis. 01-2013 https://inria.hal.science/hal-01693184
- [83] D. Mateus, C. Wachinger, S. Atasoy, L. Schwarz, N. Navab. Learning Manifolds: Design Analysis for Medical Applications. In Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. Advances in Bioinformatics and Biomedical Engineering. Editor Suzuki, Kenji. Publisher IGI Global, 2012. ISBN1466600608, 9781466600607. 31-01-2012 https://inria.hal.science/hal-01690334
- [84] A. Sharma, R. Horaud, D. Mateus. 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching. In Image Processing and Analysing With Graphs: Theory and Practice. 07-2012 https://inria.hal.science/inria-00590273v2
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