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SIEL

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

Expertises équipes

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

Mira Rizkallah

Enseignant chercheur / Enseignante chercheuse – Maître / maîtresse de conférences

Equipe : SIMS

Employeur : Centrale Nantes

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Publications déposées sur HAL

Nombre de publications retournées : 26

Revues internationales avec comité de lecture

[1] M. Rizkallah, T. Maugey, C. Guillemot. Prediction and Sampling with Local Graph Transforms for Quasi-Lossless Light Field Compression, in IEEE Transactions on Image Processing, pp. 1-13,

[2] M. Rizkallah, X. Su, T. Maugey, C. Guillemot. Geometry-Aware Graph Transforms for Light Field Compact Representation, in IEEE Transactions on Image Processing, pp. 1-15, DOI

[3] M. Rizkallah, T. Maugey, C. Guillemot. Rate-Distortion Optimized Graph Coarsening and Partitioning for Light Field Coding, in IEEE Transactions on Image Processing, vol. 30, pp. 5518 – 5532, DOI

[4] 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

[5] 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

[6] 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

[7] 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

[8] M. Sarkis, M. Rizkallah, S. Moussaoui. Force-Driven Graph Learning and Classification, in IEEE Signal Processing Letters, vol. 33, pp. 1546 – 1550, DOI

Conférences internationales avec comité de lecture et actes

[9] M. Rizkallah, T. Maugey, C. Yaacoub, C. Guillemot. Impact of Light Field Compression on Refocused and Extended Focus Images, in EUSIPCO 2016 – 24th European Signal Processing Conference, Budapest, Hongrie, DOI

[10] X. Su, M. Rizkallah, T. Maugey, C. Guillemot. Graph-based light fields representation and coding using geometry information, in ICIP 2017 – IEEE International Conference on Image Processing, Beijing, Chine, DOI

[11] M. Rizkallah, X. Su, T. Maugey, C. Guillemot. Graph-based Transforms for Predictive Light Field Compression based on Super-Pixels, in ICASSP 2018 – IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, DOI

[12] M. Rizkallah, F. de Simone, T. Maugey, C. Guillemot, P. Frossard. Rate Distortion Optimized Graph Partitioning for Omnidirectional Image Coding, in EUSIPCO 2018 – 26th European Signal Processing Conference, Rome, Italie,

[13] X. Su, M. Rizkallah, T. Maugey, C. Guillemot. Rate-Distortion Optimized Super-Ray Merging for Light Field Compression, in EUSIPCO 2018 – 26th European Signal Processing Conference, Rome, Italie,

[14] M. Rizkallah, T. Maugey, C. Guillemot. Graph-based Spatio-angular Prediction for Quasi-Lossless Compression of Light Fields, in DCC 2019 – Data Compression Conference, Snowbird, états-Unis, DOI

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

[16] 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,

[17] 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

[18] M. Sarkis, M. Rizkallah, S. Moussaoui. Graph Force Learning from EEG Signals for Mental Workload Classification, in 2025 IEEE Statistical Signal Processing Workshop (SSP), Edinburgh, France, DOI

[19] M. Sarkis, M. Rizkallah, S. Moussaoui. Réseaux de neurones sur graphes pour l'analyse de signaux EEG, in XXXe Colloque Francophone de Traitement du Signal et des Images Gretsi, Strasbourg, France,

[20] 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,

[21] 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

[22] 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,

Theses et HDR

[23] M. Rizkallah. Transformées basées graphes pour la compression de nouvelles modalités d’image, DOI

Autres publications

[24] C. Guziolowski, O. Thiery, M. Harzallah, M. Rizkallah, C. Chevallereau. Gender Equality in a Digital Science Research Laboratory,

[25] M. Sarkis, M. Rizkallah, S. Moussaoui. Graph Force Learning from EEG Signals for Mental Workload Classification,

[26] C. Guziolowski, O. Thiery, M. Harzallah, M. Rizkallah, C. Chevallereau. Gender Equality in a Digital Science Research Laboratory, in 2025 Gender and Technology Conference (GTC), Kollam, France, DOI

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