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Analyse et fusion de donnéesIA et apprentissage statistiqueTraitement de signaux, d’images et du langage

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Approches de segmentationExtraire l’informationMéthodes d’amélioration d’imagesModéliser et optimiser l'informationOrienter le processus d’optimisation

Diana Mateus

HDR

Enseignant chercheur / Enseignante chercheuse – Professeur / professeure des universités

Equipe : SIMS

Employeur : Centrale Nantes

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Publications déposées sur HAL

Nombre de publications retournées : 132

Revues internationales avec comité de lecture

[1] D. Mateus, L. Schwarz, N. Navab. Recognizing multiple human activities and tracking full-body pose in unconstrained environments, in Pattern Recognition, vol. 45, no. 1, pp. 11 – 23, DOI

[2] 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, vol. 30, no. 3, pp. 217 – 226, DOI

[3] S. Atasoy, D. Mateus, A. Meining, G. Yang, N. Navab. Endoscopic Video Manifolds for Targeted Optical Biopsy, in IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp. 637 – 653, DOI

[4] V. Castaneda, D. Mateus, N. Navab. Stereo Time-of-Flight with Constructive Interference, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 7, pp. 1402 – 1413, DOI

[5] F. Cuzzolin, D. Mateus, R. Horaud. Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies, in International Journal of Computer Vision, vol. 112, no. 1, pp. 43-70, DOI

[6] 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, vol. 10, no. 6, pp. 773 – 781, DOI

[7] 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, vol. 35, pp. 655 – 668, DOI

[8] B. Gutiérrez-Becker, D. Mateus, L. Peter, N. Navab. Guiding multimodal registration with learned optimization updates, in Medical Image Analysis, vol. 41, pp. 2 – 17, DOI

[9] 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 and Biology, vol. 44, no. 1, pp. 278 – 291, DOI

[10] 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, vol. 53, no. 9, pp. 1100-1106, DOI

[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, DOI

[12] 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, vol. 39, pp. 3725 – 3736, DOI

[13] 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, DOI

[14] 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, pp. 1-14, DOI

[15] 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, vol. 48, no. 4, pp. 1005-1015, DOI

[16] M. Tardy, D. Mateus. Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction, in IEEE Transactions on Medical Imaging, vol. 40, no. 10, pp. 2711-2722, DOI

[17] M. Tardy, D. Mateus. Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms, in Frontiers in Radiology, vol. 1, pp. 796078, DOI

[18] 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, vol. 75, pp. 102273, DOI

[19] 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, vol. 6, no. 6, pp. 629-640, DOI

[20] 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, vol. 67, no. 15, pp. 155011, DOI

[21] 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, vol. 229, pp. 107318, DOI

[22] T. Godefroy, G. Frécon, A. Asquier-Khati, D. Mateus, R. Lecomte, M. Rizkallah, N. Piriou, B. Jamet, T. Le Tourneau, A. Pallardy, D. Boutoille, T. Eugène, T. Carlier. 18F-FDG-Based Radiomics and Machine Learning, in JACC: Cardiovascular Imaging, vol. 16, no. 7, pp. 951 – 961, DOI

[23] T. Carlier, G. Frécon, D. Mateus, M. Rizkallah, F. Kraeber-Bodéré, S. Kanoun, P. Blanc-Durand, E. Itti, S. Le Gouill, R. Casasnovas, C. Bodet-Milin, C. Bailly. Prognostic Value of 18 F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study, in Journal of Nuclear Medicine, vol. 65, no. 1, pp. 156-162, DOI

[24] O. Thiery, M. Rizkallah, C. Bailly, C. Bodet-Milin, E. Itti, R. Casasnovas, S. Le Gouill, T. Carlier, D. Mateus. PET-based lesion graphs meet clinical data: An interpretable cross-attention framework for DLBCL treatment response prediction, in Computerized Medical Imaging and Graphics, vol. 120, pp. 102481, DOI

[25] G. Pelluet, M. Rizkallah, M. Tardy, D. Mateus. Enhancing breast cancer screening: Unveiling explainable cross-view contributions in dual-view mammography with Sparse Bipartite Graphs Attention Networks, in Computerized Medical Imaging and Graphics, vol. 125, pp. 102620, DOI

[26] B. Jamet, S. Ahrari, S. Zweegman, A. Perrot, C. Hulin, D. Caillot, T. Facon, X. Leleu, K. Belhadj, E. Itti, L. Karlin, C. Bailly, M. Levin, M. Minnema, C. Bodet-Milin, B. de Keizer, J. Corre, P. Sonneveld, D. Mateus, P. Moreau, C. Touzeau, F. Kraeber-Bodéré, T. Carlier. FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial, in Blood Advances, vol. 10, no. 11, pp. 3787-3798, DOI

[27] J. Huet, L. Piecuch, E. Gossard, A. Boureau, D. Mateus, A. Nordez. Three-dimensional ultrasound shape analysis reveals distinct structural patterns in muscle aging and sarcopenia, in Experimental Gerontology, vol. 223, pp. 113258, DOI

Conférences internationales avec comité de lecture et actes

[28] D. Mateus, J. Avina Cervantes, M. Devy. Robot Visual Navigation in Semi-structured Outdoor Environments, in 2005 IEEE International Conference on Robotics and Automation, Barcelona, Espagne, DOI

[29] 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, New York, états-Unis, DOI

[30] 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, Austin, TX, états-Unis, DOI

[31] 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, Rio de Janeiro, Brésil, DOI

[32] 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, Rio de Janeiro, Brésil, DOI

[33] 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, Rio de Janeiro, Brésil, DOI

[34] 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, Anchorage, états-Unis, DOI

[35] 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, Anchorage, états-Unis, DOI

[36] 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, Venice, Italie, DOI

[37] 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, Londres, Royaume-Uni,

[38] 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, Kyoto, Japon, DOI

[39] L. Schwarz, D. Mateus, N. Navab. Discriminative Human Full-Body Pose Estimation from Wearable Inertial Sensor Data, in Modelling the Physiological Human. 3DPH 2009., Zermatt, Suisse,

[40] 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, Rotterdam, France, DOI

[41] 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, Norrköping, Suède, DOI

[42] 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, Port d'Andratx, Espagne,

[43] 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, Aberystwyth, Royaume-Uni, DOI

[44] S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G. Yang, N. Navab. , in International Conference in Medical Imaging and Computer Aided Interventions (MICCAI), Beijing, Chine,

[45] 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, Queensland, Nouvelle-Zélande,

[46] 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), Kona, états-Unis, DOI

[47] V. Castaneda, D. Mateus, N. Navab. SLAM combining ToF and high-resolution cameras, in 2011 IEEE Workshop on Applications of Computer Vision (WACV), Kona, états-Unis, DOI

[48] 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, Lake Buena Vista, états-Unis, DOI

[49] 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), Toronto, Canada,

[50] 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, Toronto, Canada,

[51] D. Mateus, V. Castaneda, N. Navab. Stereo time-of-flight, in IEEE International Conference on Computer Vision (ICCV), Barcelona, Espagne, DOI

[52] 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), Barcelona, Espagne, DOI

[53] S. Atasoy, D. Mateus, A. Meining, G. Yang, N. Navab. , in International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), Nice, France,

[54] 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, Vila Moura, Portugal,

[55] 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), Beijing, Chine, DOI

[56] N. Rieke, C. Hennersperger, D. Mateus, N. Navab. Ultrasound interactive segmentation with tensor-graph methods, in 11th International Symposium on Biomedical Imaging (ISBI), Beijing, Chine, DOI

[57] 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, Boston, états-Unis,

[58] 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), Boston, états-Unis,

[59] 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, Munich, Allemagne,

[60] C. Hennersperger, M. Baust, D. Mateus, N. Navab. Computational Sonography, in International Conference on Medical Image Computing and Computer Aided Interventions (MICCAI), Munich, Allemagne,

[61] 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, Munich, Allemagne,

[62] 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), Athenes, Grèce,

[63] 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), Athènes, Grèce, DOI

[64] 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, Tandil, Argentine,

[65] 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, Orlando, états-Unis, DOI

[66] 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), Granada, Espagne, DOI

[67] 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, Sydney, Australie, DOI

[68] M. Tardy, B. Scheffer, D. Mateus. Breast Density Quantification Using Weakly Annotated Dataset, in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), Venice, Italie, DOI

[69] 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), Rennes, France,

[70] 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, Rennes, France, DOI

[71] 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, London, Royaume-Uni,

[72] 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, Shenzen, Chine, DOI

[73] L. Zhou, N. Heller, Y. Shi, Y. Xiao, R. Sznitman, V. Cheplygina, D. Mateus, E. Trucco, X. Hu, D. Chen, M. Chabanas, H. Rivaz, I. Reinertsen. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, in Satellite workshop CURIOUS /MICCAI, Shenzhen, Chine, DOI

[74] 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, Shenzen, Chine, DOI

[75] M. Tardy, D. Mateus. Lightweight U-Net for high-resolution breast imaging, in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, états-Unis,

[76] 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), Munich, Allemagne, DOI

[77] M. Tardy, D. Mateus. Improving Mammography Malignancy Segmentation by Designing the Training Process, in Medical Imaging with Deep Learning, Montreal, Canada,

[78] 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, Montréal, Canada, DOI

[79] 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,, Lima, Pérou, DOI

[80] H. Carrillo Lindado, M. Millardet, T. Carlier, D. Mateus. Low-count PET image reconstruction with Bayesian inference over a Deep Prior, in Image Processing, Online Only, états-Unis, DOI

[81] M. Tardy, D. Mateus. Trainable Summarization to Improve Breast Tomosynthesis Classification, in International Conference on Medical Image Computing and Computer Assisted Intervention, Strasbourg, France, DOI

[82] 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, Strasbourg (virtuel), France,

[83] G. Pelluet, M. Rizkallah, O. Acosta, D. Mateus. Unsupervised Multimodal Supervoxel Merging towards Brain Tumor Segmentation, in BrainLes 2021 MICCAI workshop, Strasbourg, France, DOI

[84] 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, Milan, Italie, DOI

[85] 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, Cartagena de Indias, Colombie,

[86] L. Piecuch, V. Gonzales Duque, A. Sarcher, E. Hollville, A. Nordez, G. Rabita, G. Guilhem, D. Mateus. Muscle volume quantification: guiding transformers with anatomical priors, in Shape in Medical Imaging (ShapeMI) workshop from MICCAI, Vancouver (BC), Canada, DOI

[87] Y. Zhang, C. Huneau, J. Idier, D. Mateus. Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models, in Deep Generative Models workshop at MICCAI 2023, Vancouver, Canada, DOI

[88] 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, Vancouver, Canada, DOI

[89] A. Merasli, T. Carlier, D. Mateus, S. Moussaoui, S. Stute. The influence of input and skip connections in PET reconstruction with Deep Image Prior, in 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference, Vancouver (BC), Canada,

[90] Y. Zhang, C. Huneau, J. Idier, D. Mateus. Ultrasound Imaging based on the Variance of a Diffusion Restoration Model, in 32nd European Signal Processing Conference (EUSIPCO 2024), Lyon, France, DOI

[91] M. Monvoisin, Y. Zhang, D. Mateus. Compact Implicit Neural Representations for Plane Wave Images, in 2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium (UFFC-JS), Taipei, Taïwan, DOI

[92] L. Piecuch, J. Huet, A. Frouin, A. Nordez, A. Boureau, D. Mateus. UNSUPERVISED ANOMALY DETECTION ON IMPLICIT SHAPE REPRESENTATIONS FOR SARCOPENIA DETECTION, in International Symposium on Biomedical Imaging, Houston, états-Unis, DOI

[93] L. El Hadi, M. Monvoisin, D. Mateus. Détection non supervisée d'aiguilles en échographie 3D avec une représentation neuronale implicite, in Colloque GRETSI 2025, Strasbourg, France,

[94] D. Chamseddine, S. Ahrari, M. Rizkallah, T. Carlier, D. Mateus. Enhancing Survival Outcomes in Head and Neck Cancer through Joint HPV Classification and Tumor Segmentation, in Lecture Notes in Computer Science (LNCS), MICCAI 2025, Daegeon, Corée du Sud,

[95] M. Monvoisin, L. Piecuch, B. Texier, C. Hémon, A. Barateau, J. Huet, A. Nordez, A. Boureau, J. Nunes, D. Mateus. Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation, in International Workshop on Shape in Medical Imaging – MICCAI 2025, Daejeon, Corée du Sud, DOI

[96] O. Thiery, M. Rizkallah, H. Laribi, M. Vallières, T. Carlier, D. Mateus. Population-Graph Post-hoc Correction of Survival Predictions for Improved Risk Stratification, in GRAIL/TMI/HGMIA@MICCAI, Daejeon, Corée du Sud, DOI

[97] Y. Moussaoui, S. Moussaoui, D. Mateus, T. Carlier, S. Stute. Learning Regularization Parameters for PET Image Reconstruction, in IEEE Nuclear Science Symposium Medical Imaging Conference and Room Temperature Semiconductor Conference, Yokohama, Japon,

[98] F. Basbous, D. Mateus, F. Poirier, F. Haddad. HYBRID AUTOENCODER-ISOLATION FOREST APPROACH FOR TIME SERIES ANOMALY DETECTION IN C70XP CYCLOTRON OPERATION DATA AT ARRONAX, in CYC2025 – International Conference on Cyclotrons and their Applications, Chengdu, Chine,

[99] A. Dauny, H. Han, A. Mukhamedjanova, A. Faubert, B. Pascal, D. Mateus, S. Wang. In vivo OCT angiography of mouse ovary, in SPIE Photonics West, San Francisco, états-Unis,

[100] Y. Moussaoui, D. Mateus, L. El Hadi, S. Moussaoui, S. Stute, N. Taheri. SPINR: SELF-SUPERVISED PET RECONSTRUCTION USING IMPLICIT NEURAL REPRESENTATIONS, in IEEE International Symposium on Biomedical Imaging, London, Royaume-Uni,

[101] D. Chamseddine, M. Rizkallah, S. Ahrari, T. Carlier, D. Mateus. SurvSOLM -Interpretable Survival Self-Organized Longitudinal Maps for Outcome Predictions, in Medical Image Understanding and Analysis – MIUA, Dublin (Ireland), Irlande,

Conférences nationales avec comité de lecture et actes

[102] 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, Paris, France,

[103] 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, Nantes, France,

[104] 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), Nantes, France,

Ouvrages – Chapitres d'ouvrages et directions d'ouvrages

[105] D. Mateus, C. Wachinger, S. Atasoy, L. Schwarz, N. Navab. Learning Manifolds: Design Analysis for Medical Applications,

[106] A. Sharma, R. Horaud, D. Mateus. 3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching,

[107] 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,

[108] 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, DOI

[109] C. Fourcade, M. Rubeaux, D. Mateus. Using Elastix to Register Inhale/Exhale Intrasubject Thorax CT: A Unsupervised Baseline to the Task 2 of the Learn2Reg Challenge, DOI

Theses et HDR

[110] D. Mateus. Méthodes Spectrales pour la Modélisation d'Objets Articulés à Partir de Vidéos Multiples,

Autres publications

[111] 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, Vannes, France,

[112] D. Stoyanov, Z. Taylor, S. Balocco, R. Sznitman, A. Martel, L. Maier-Hein, L. Duong, G. Zahnd, S. Demirci, S. Albarqouni, S. Lee, S. Moriconi, V. Cheplygina, D. Mateus, E. Trucco, E. Granger, P. Jannin. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, in Lecture Notes in Computer Science, vol. 11043, DOI

[113] 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, Sydney, Australie, DOI

[114] 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 …, Paris, France,

[115] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I (machine learning methodologies), in Lecture Notes in Computer Science, vol. Proceedings, Part I – machine learning methodologies, no. 12261, in MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Pérou, DOI

[116] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II (image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks), in Lecture Notes in Computer Science, vol. Proceedings, Part II – image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks, no. 12262, in MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Pérou, DOI

[117] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III (CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis), in Lecture Notes in Computer Science, vol. Part III – CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis, no. 12263, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Pérou, DOI

[118] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV (segmentation; shape models and landmark detection), in Lecture Notes in Computer Science, vol. Proceedings, Part IV – segmentation; shape models and landmark detection, no. 12264, pp. XXXVII, 831, in MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Pérou,

[119] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V (biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology), in Lecture Notes in Computer Science, vol. 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V, no. 12265, pp. XXXVII, 811, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Pérou,

[120] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging), in Lecture Notes in Computer Science, vol. 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI, no. 12266, pp. XXXVII, 819, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Pérou,

[121] A. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. Zuluaga, S. Zhou, L. Joskowicz, D. Racoceanu. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII (brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography), in Lecture Notes in Computer Science, vol. Part VII – brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography, no. 12267, pp. XXXVII, 817, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, Lima, Pérou,

[122] F. Poirier, D. Mateus, J. Rioult, C. Lassalle. First anomalies exploration from data mining and machine learning at the ARRONAX cyclotron C70XP, in 14th International Particle Accelerator Conference : IPAC23, Venise, Italie, DOI

[123] L. Piecuch, V. Gonzalez Duque, A. Nordez, G. Guilhem, L. Lacourpaille, A. Sarcher, C. Giroux, D. Mateus. Deep learning-based lower-limb muscles segmentation for volume quantification with transformers, in Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM) 2023, Paris, France,

[124] Y. Moussaoui, D. Mateus, S. Moussaoui, T. Carlier, S. Stute. Networks for the Prediction of the Regularization Parameters in PET Reconstruction, in Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Grenoble, France,

[125] L. Piecuch, A. Maurel, R. Macchi, E. Filleur, C. Giroux, H. Enzo, A. Nordez, G. Rabita, G. Guilhem, A. Boureau, D. Mateus. Implicit muscle shape representations, in Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM) 2025, Nice, France,

[126] Y. Moussaoui, D. Mateus, N. Taheri, S. Moussaoui, T. Carlier, S. Stute. Implicit Neural Network for End-to-End PET Reconstruction, in Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Nice, France,

[127] Y. Zhang, C. Huneau, J. Idier, D. Mateus. Image Despeckling based on the Variance of Diffusion Restoration Models: an Application to SAR and Medical Ultrasound,

[128] F. Basbous, D. Mateus, F. Poirier, F. Haddad. Hybrid Autoencoder-Isolation Forest Approach for Time Series Anomaly Detection in C70XP Cyclotron Operation Data at ARRONAX, in CYC2025 – International Conference on Cyclotrons and their Applications, Chengdu, China, Chine,

[129] M. Monvoisin, L. Piecuch, B. Texier, A. Barateau, J. Huet, A. Nordez, A. Boureau, J. Nunes, D. Mateus. Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation, in Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM) 2026, Lyon, France,

[130] F. Basbous, F. Poirier, D. Mateus, F. Haddad. Transfer Learning for Generalising a Hybrid Autoencoder-Isolation Forest Model for Time Series Anomaly Detection in ARRONAX Cyclotron Operational Data, in IPAC'26 – the 17th International Particle Accelerator Conference, Deauville, France,

[131] A. Dauny, B. Pascal, H. Han, S. Wang, D. Mateus. FID versus MMD Minimization for Real-Synthetic Domain Alignment in Vessel OCT Image Synthesis,

[132] A. Akter, M. Dalla Mura, D. Mateus, D. Al Chanti. Une meilleure localisation des explications grâce à la régularisation n’implique pas une meilleure fidélité,

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