Professor in Biomedical Image and Data Computing at Sorbonne University, Paris, and PI at ARAMIS INRIA team / Paris Brain Institute (ICM / Piti-Salpêtrière Hospital), my areas of interest are Medical Image Analysis and Pattern Recognition, my research focusing mainly on Computational Pathology and its Integrative aspects.
EDUCATION
2006 - HDR (Habilitation à Diriger des Recherches), Control and Computer Sciences - University of Franche-Comté, Besançon, France
1997 - Ph.D. - Control and Computer Sciences - University of Franche-Comté, Besançon, France
1993 - M.Sc. (Master of Science) - Control Sciences - University of Technology of Belfort-Montbéliard, France
1992 - Dipl. Ing. (M.Eng. - Master of Engineering) - Mechatronics & Mechanics - Politehnica University of Timisoara, Romania
City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams Ilias Sarbout, Mehdi Ounissi, Théo Cazenave-Coupet, Dan Milea, Daniel Racoceanu Proceedings of the Aaai Conference on Artificial Intelligence, 2026 We present City Of Light, a Unity-based, city-scale 116 km² simulator of Paris for high-throughput embodied AI research. COL fuses open geographic information system sources into geo-anchored, per-tile meshes and provides a configurable, stochastic runtime with controllable traffic and pedestrians. Agents receive frame-synchronized multi-sensor observations (RGB, depth, normals, semantics) and execute step-synchronized actions to navigate the environment. To support high-rate vision pipelines, we introduce TURBO, a Unity-Python bridge that streams multi-camera observations and allows control at up to 1300 FPS, achieving higher throughput than ML-Agents in our benchmark. We also provide a Street View Digital Twin that aligns simulator viewpoints with corresponding real-world panoramas for frame-accurate visual comparison and quantitative matching. COL enables fast scripting, large-scale data collection, and reinforcement learning in geo-anchored urban settings.
Unravelling the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression Gabriel Jimenez, Leopold Hebert-Stevens, Susana Boluda, Benoît Delatour, Lev Stimmer, et al. Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2025 In this study, we proposed and evaluated a graph-based framework to assess variations in Alzheimer’s disease (AD) neuropathologies, focusing on classic (cAD) and rapid (rpAD) progression forms. Histopathological images are converted into tau-pathology-based (i.e., amyloid plaques and tau tangles) graphs, and derived metrics are used in a machine-learning classifier. This classifier incorporates SHAP value explainability to differentiate between cAD and rpAD. Furthermore, we tested graph neural networks (GNNs) to extract topological embeddings from the graphs and use them in classifying the progression forms of AD. The analysis demonstrated denser networks in rpAD and a distinctive impact on brain cortical layers: rpAD predominantly affects middle layers, whereas cAD influences both superficial and deep layers of the same cortical regions. These results suggest a unique neuropathological network organization for each AD variant.
Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images Mehdi Ounissi, Ilias Sarbout, Jean-Pierre Hugot, Christine Martinez-Vinson, Dominique Berrebi, et al. Plos Computational Biology, 2025 Chemical staining methods, while reliable, are time consuming and can be resource-intensive, involving costly chemical reagents and raising environmental concerns. This underscores the compelling need for alternative solutions such as virtual staining, which not only accelerates the diagnostic process but also enhances the flexibility of stain applications without the associated physical and chemical costs. Generative artificial intelligence technologies prove to be immensely useful in addressing these challenges. However, in healthcare, particularly within computational pathology, the high-stakes nature of decisions complicates the adoption of these tools due to their often opaque processes. Our work introduces an innovative approach that harnesses generative models for virtual stain transformations, improving performance, trustworthiness, scalability, and adaptability within computational pathology. The core of the proposed methodology involves a singular Hematoxylin and Eosin (H&E) encoder that supports multiple stain decoders. This design prioritizes critical regions in the latent space of H&E tissues, leading to a richer representation that enables precise synthetic stain generation by the decoders. Tested to simultaneously generate eight different stains from a single H&E slide, our method also offers significant scalability benefits for routine use by loading only necessary model components during production. We integrate label-free knowledge during training, using loss functions and regularization to minimize artifacts, thereby enhancing the accuracy of virtual staining in both paired and unpaired settings. To build trust in these synthetic stains, we employ a real-time self-inspection methodology using trained discriminators for each stain type, providing pathologists with confidence heatmaps to aid in their evaluations. In addition, we perform automatic quality checks on new H&E slides to ensure that they conform to the trained H&E distribution, guaranteeing the generation of high-quality synthetic stained slides. Recognizing the challenges pathologists face in adopting new technologies, we have encapsulated our method in an open-source, cloud-based proof-of-concept system. This system enables users to easily and virtually stain their H&E slides through a browser, eliminating the need for specialized technical knowledge and addressing common hardware and software challenges. It also facilitates real-time user feedback integration. Lastly, we have curated a novel dataset comprising eight different paired H&E/stains related to pediatric Crohn’s disease at diagnosis, providing 30 whole slide images (WSIs) for each stain set (total of 480 WSIs) to stimulate further research in computational pathology.
Artificial Intelligence-Based Detection of Central Retinal Artery Occlusion Within 4.5Hours on Standard Fundus Photographs Ayse Gungor, Ilias Sarbout, Aubrey L. Gilbert, Steffen Hamann, Pierre Lebranchu, et al. Journal of the American Heart Association, 2025 Background Prompt diagnosis of acute central retinal artery occlusion (CRAO) is crucial for therapeutic management and stroke prevention. However, most stroke centers lack onsite ophthalmic expertise before considering fibrinolytic treatment. This study aimed to develop, train, and test a deep learning system to detect hyperacute CRAO on retinal fundus photographs within the critical 4.5‐hour treatment window and up to 24 hours after visual loss to aid in secondary stroke prevention. Methods Our retrospective, cross‐sectional study included 1322 color fundus photographs from 771 patients with acute visual loss due to CRAO, central retinal vein occlusion, nonarteritic anterior ischemic optic neuropathy, and healthy controls. Photographs were collected from 9 expert neuro‐ophthalmology centers in 6 countries, including 3 randomized clinical trials. Training included 1039 photographs (517 patients), followed by testing on 2 data sets: (1) hyperacute CRAO (54 photographs, 54 patients) and (2) CRAO within 24 hours after visual loss (110 photographs, 109 patients). Results The deep learning system achieved an area under the receiver operating characteristic curve of 0.96 (95% confidence interval (CI), 0.95–0.98), a sensitivity of 92.6% (95% CI, 87.0–98.0), and a specificity of 85.0% (95% CI, 81.8–92.8) for detecting CRAO at hyperacute stage, with similar results within 24 hours. The deep learning system outperformed stroke neurologists on a subset of hyperacute testing data set (120 photographs, 120 patients). Conclusions A deep learning system can accurately detect hyperacute CRAO on retinal photographs within a time window compatible with urgent fibrinolysis. If further validated, such systems could improve patient selection for fibrinolytic trials and optimize secondary stroke prevention. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06390579.
PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies Mehdi Ounissi, Morwena Latouche, Daniel Racoceanu Scientific Reports, 2024 Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat.
Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software Lea Ingrassia, Susana Boluda, Marie-Claude Potier, Stéphane Haïk, Gabriel Jimenez, et al. Journal of Neuropathology and Experimental Neurology, 2024 Neuropathological diagnosis of Alzheimer disease (AD) relies on semiquantitative analysis of phosphorylated tau-positive neurofibrillary tangles (NFTs) and neuritic plaques (NPs), without consideration of lesion heterogeneity in individual cases. We developed a deep learning workflow for automated annotation and segmentation of NPs and NFTs from AT8-immunostained whole slide images (WSIs) of AD brain sections. Fifteen WSIs of frontal cortex from 4 biobanks with varying tissue quality, staining intensity, and scanning formats were analyzed. We established an artificial intelligence (AI)-driven iterative procedure to improve the generation of expert-validated annotation datasets for NPs and NFTs thereby increasing annotation quality by >50%. This strategy yielded an expert-validated annotation database with 5013 NPs and 5143 NFTs. We next trained two U-Net convolutional neural networks for detection and segmentation of NPs or NFTs, achieving high accuracy and consistency (mean Dice similarity coefficient: NPs, 0.77; NFTs, 0.81). The workflow showed high generalization performance across different cases. This study serves as a proof-of-concept for the utilization of proprietary image analysis software (Visiopharm) in the automated deep learning segmentation of NPs and NFTs, demonstrating that AI can significantly improve the annotation quality of complex neuropathological features and enable the creation of highly precise models for identifying these markers in AD brain sections.
Preface Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
Preface Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
RECENT SCHOLAR PUBLICATIONS
From synthetic navigation data to real-world mobility cues: Reinforcement learning for sensory substitution in visual impairment I Sarbout, M Ounissi, D Milea, D Racoceanu Array, 100861 , 2026 2026
City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams I Sarbout, M Ounissi, T Cazenave-Coupet, D Milea, D Racoceanu Proceedings of the AAAI Conference on Artificial Intelligence 40 (48), 41679 … , 2026 2026 Citations: 1
Normalization Bias in Morpho-Transcriptomic Prediction S Ruyter, R Dorent, D Racoceanu Medical Imaging with Deep Learning-Short Papers , 2026 2026
Visual Prostheses in the Era of Artificial Intelligence Technology I Sarbout, A Gungor, M Ounissi, S Zaher, M Ptito, R Kupers, D Racoceanu, ... Eye and Brain, 95-113 , 2025 2025 Citations: 3
Multimodal integration of data characterizing the evolution of the gutbrain axis during the prodromal phase of Parkinson's disease in a rat model M Hamadache, L Mouton, D Barriere, C Keller, C Chassain, G Pages, ... 2025
Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images M Ounissi, I Sarbout, JP Hugot, C Martinez-Vinson, D Berrebi, ... PLOS Computational Biology 21 (10), e1013516 , 2025 2025 Citations: 8
ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization C Zhao, J Li, Q Zhao, J Bai, S Boluda, B Delatour, L Stimmer, ... IRBM, 100913 , 2025 2025
Reflections on the Use of Generative AI for Research Professions S Arias, M Bergmann, F Campillo, MA Enard, C Fabre, F Garcia, B Guedj, ... Inria , 2025 2025
Artificial Intelligence‐Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs A Gungor, I Sarbout, AL Gilbert, S Hamann, P Lebranchu, C Hobeanu, ... Journal of the American Heart Association 14 (13), e041441 , 2025 2025 Citations: 9
Deep learning-based classification of acute anterior optic neuropathies in the Emergency Room, on images acquired with a portable nonmydriatic camera: a prospective study S Zaher, A Gungor, I Sarbout, S Croitoru, D Raicu, B Touzani, L Senicourt, ... Investigative Ophthalmology & Visual Science 66 (8), 5438-5438 , 2025 2025
Diffusion Models for Morphology-Guided Transcriptomics: A Computational Framework S Ruyter, M Ounissi, D Racoceanu ECDP 2025-European Congress on Digital Pathology , 2025 2025
Longitudinal MRI Assessment of Brain Changes in Parkinson’s Disease E Kozlowski, R Valabregue, S Ouarab, M Didier, R Gaurav, JB Pérot, ... Parkinsonism & Related Disorders 134 , 2025 2025
Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg J Zou, J Li, G Jimenez, Q Zhao, D Racoceanu, M Cosarinsky, E Ferrante, ... arXiv preprint arXiv:2504.15667 , 2025 2025
Unravelling the topographical organization of brain lesions in variants of Alzheimer's disease progression G Jimenez, L Hebert-Stevens, S Boluda, B Delatour, L Stimmer, ... Medical Imaging 2025: Digital and Computational Pathology 13413, 108-115 , 2025 2025
Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information LE Marin, DI Zavaleta-Guzman, JI Gutierrez-Garcia, D Racoceanu, ... Discover Oncology 16 (1), 128 , 2025 2025 Citations: 7
Réflexions sur l'usage de l'IA générative pour les métiers de la recherche S Arias, M Bergmann, F Campillo, MA Enard, C Fabre, F Garcia, B Guedj, ... Inria , 2025 2025
AI-based Detection of Central Retinal Artery Occlusion within 4.5 hours on Standard Fundus Photographs A Gungor, I Sarbout, AL Gilbert, S Hamann, P Lebranchu, C Hobeanu, ... medRxiv, 2024.12. 19.24319390 , 2024 2024 Citations: 2
Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software L Ingrassia, S Boluda, MC Potier, S Haïk, G Jimenez, A Kar, D Racoceanu, ... Journal of Neuropathology & Experimental Neurology 83 (9), 752-762 , 2024 2024 Citations: 5
From histopathology images to molecular characterisation of tumours: The artificial intelligence path. V Popovici, D Racoceanu Recent Advances in Histopathology 27 , 2024 2024
Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach P Kumar, M Lacroix, P Dupré, J Arslan, L Fenou, B Orsetti, L Le Cam, ... Physics in Medicine & Biology 69 (12), 125023 , 2024 2024 Citations: 8
MOST CITED SCHOLAR PUBLICATIONS
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer B Ehteshami Bejnordi, M Veta, P Johannes van Diest, B Van Ginneken, ... Jama 318 (22), 2199-2210 , 2017 2017 Citations: 4280
Gland segmentation in colon histology images: The glas challenge contest K Sirinukunwattana, JPW Pluim, H Chen, X Qi, PA Heng, YB Guo, ... Medical image analysis 35, 489-502 , 2017 2017 Citations: 1210
Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential H Irshad, A Veillard, L Roux, D Racoceanu IEEE reviews in biomedical engineering 7, 97-114 , 2013 2013 Citations: 889
Mitosis detection in breast cancer histological images An ICPR 2012 contest R Ludovic, R Daniel, L Nicolas, K Maria, I Humayun, K Jacques, ... Journal of pathology informatics 4 (1), 8 , 2013 2013 Citations: 414
Deep learning in the biomedical applications: Recent and future status R Zemouri, N Zerhouni, D Racoceanu Applied Sciences 9 (8), 1526 , 2019 2019 Citations: 266
Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography A Depeursinge, D Racoceanu, J Iavindrasana, G Cohen, A Platon, ... Artificial intelligence in medicine 50 (1), 13-21 , 2010 2010 Citations: 241
Efficient deep learning model for mitosis detection using breast histopathology images M Saha, C Chakraborty, D Racoceanu Computerized Medical Imaging and Graphics 64, 29-40 , 2018 2018 Citations: 240
Recurrent radial basis function network for time-series prediction R Zemouri, D Racoceanu, N Zerhouni Engineering Applications of Artificial Intelligence 16 (5-6), 453-463 , 2003 2003 Citations: 182
Best practice recommendations for the implementation of a digital pathology workflow in the anatomic pathology laboratory by the European Society of Digital and Integrative … F Fraggetta, V L’imperio, D Ameisen, R Carvalho, S Leh, TR Kiehl, ... Diagnostics 11 (11), 2167 , 2021 2021 Citations: 143
Automatic breast cancer grading of histopathological images JR Dalle, WK Leow, D Racoceanu, AE Tutac, TC Putti 2008 30th Annual International Conference of the IEEE Engineering in … , 2008 2008 Citations: 135
Contribution à la surveillance des systèmes de production à l'aide des réseaux de neurones dynamiques: Application à la e-maintenance R Zemouri Université de Franche-Comté , 2003 2003 Citations: 126
Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach H Irshad, S Jalali, L Roux, D Racoceanu, LJ Hwee, G Le Naour, F Capron Journal of pathology informatics 4 (2), 12 , 2013 2013 Citations: 123
Time-efficient sparse analysis of histopathological whole slide images CH Huang, A Veillard, L Roux, N Loménie, D Racoceanu Computerized medical imaging and graphics 35 (7-8), 579-591 , 2011 2011 Citations: 106
Deep learning for semantic segmentation vs. classification in computational pathology: application to mitosis analysis in breast cancer grading G Jiménez, D Racoceanu Frontiers in bioengineering and biotechnology 7, 145 , 2019 2019 Citations: 96
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, ... Springer Nature , 2020 2020 Citations: 86
Nuclear pleomorphism scoring by selective cell nuclei detection. JR Dalle, H Li, CH Huang, WK Leow, D Racoceanu, TC Putti WACV , 2009 2009 Citations: 86
Perceived age and life style. The specific contributions of seven factors involved in health and beauty VG Clatici, D Racoceanu, C Dalle, C Voicu, L Tomas-Aragones, ... Maedica 12 (3), 191 , 2017 2017 Citations: 74
Global energy outlook 2023: sowing the seeds of an energy transition D Raimi, Y Zhu, RG Newell, BC Prest, A Bergman Resources for the Future 1 (1), 1-44 , 2023 2023 Citations: 73
Knowledge-guided semantic indexing of breast cancer histopathology images AE Tutac, D Racoceanu, T Putti, W Xiong, WK Leow, V Cretu 2008 international conference on biomedical engineering and informatics 2 … , 2008 2008 Citations: 67
New trends to support independence in persons with mild dementia–a mini-review M Mokhtari, H Aloulou, T Tiberghien, J Biswas, D Racoceanu, P Yap Gerontology 58 (6), 554-563 , 2012 2012 Citations: 65
