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Diana MATEUS

ENSEIGNANT-CHERCHEUR

Professeur des universités

: Diana.Mateusatls2n.fr

Page pro : https://dcml-cn.github.io/

Adresse :

Centrale Nantes ( CN )
Petit Port
1, rue de la Noë
BP 92101
44321 NANTES Cedex 3

Batiment S, étage 3, bureau 309

Publications 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. 11-01-2022
https://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. Institute of Electrical and Electronics Engineers, 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. 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

Conférences internationales avec comité de lecture et actes (COMM_INT)

[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] O. Thiery, M. Rizkallah, C. Bailly, C. Bodet-Milin, E. Itti, R. Casasnovas, S. Le Gouill, T. Carlier, D. Mateus. Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images. In International Conference on Medical Image Computing and Computer-Assisted Intervention, octobre 2023, Vancouver, Canada.
https://hal.science/hal-04254481

[24] A. Merasli, T. Liu, T. Carlier, D. Mateus, M. Millardet, S. Moussaoui, S. Stute. Nested ADMM for PET reconstruction with two constraints: Deep Image Prior and non-negativity in projection space. In 2022 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, novembre 2022, Milan, Italie.
https://hal.science/hal-03930288

[25] 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

[26] 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

[27] 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

[28] 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

[29] 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

[30] 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

[31] 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

[32] 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

[33] 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

[34] 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

[35] 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

[36] 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

[37] 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

[38] 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

[39] 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

[40] 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

[41] 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

[42] 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

[43] 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

[44] 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

[45] 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

[46] 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

[47] 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

[48] 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

[49] 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

[50] 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

[51] 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

[52] 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

[53] 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

[54] 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

[55] 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

[56] 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

[57] 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

[58] 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

[59] 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

[60] 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

[61] 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

[62] 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

[63] 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

[64] 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

[65] 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

[66] 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

[67] 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

[68] 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

[69] 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

[70] 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

[71] 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

[72] 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

[73] 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

[74] 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

[75] 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

[76] 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

[77] 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

[78] 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

[79] 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

Conférences nationales avec comité de lecture et actes (COMM_NAT)

[80] 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

[81] 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

[82] 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

Ouvrages - Chapitres d‘ouvrages et directions d‘ouvrages (OUV)

[83] 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

[84] 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

[85] 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

[86] 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

Theses et HDR (THESE)

[87] D. Mateus. Méthodes Spectrales pour la Modélisation d'Objets Articulés à Partir de Vidéos Multiples. Thèses : Institut National Polytechnique de Grenoble - INPG.
https://theses.hal.science/tel-00447103

Autres publications (AUTRES)

[88] L. Morvan, D. Mateus, C. Bailly, B. Jamet, C. Bodet-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 ..., 2019, Paris, France.
https://inserm.hal.science/inserm-02100680

[89] M. Millardet, S. Moussaoui, D. Mateus, J. Idier, T. Carlier. Étude comparative d'AML, NEGML et OSEM sur des données 90Y-TEP expérimentales et cliniques en utilisant CASToR. In Journées du Cancéropôle Grand Ouest, juillet 2018, Vannes, France.
https://hal.science/hal-01953061

[90] M. Millardet, S. Moussaoui, D. Mateus, J. Idier, T. Carlier. Local-mean preserving post-processing for non-negativity enforcement in 90Y-PET imaging. In Medical Imaging Conference, novembre 2018, Sydney, Australie.
https://hal.science/hal-01951847