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
From synthetic navigation data to real-world mobility cues: Reinforcement learning for sensory substitution in visual impairment Ilias Sarbout, Mehdi Ounissi, Dan Milea, Daniel Racoceanu Array, 2026 Limited access to large-scale, task-specific navigation data remains a major obstacle to designing lightweight, real-time sensory-substitution devices (SSDs) for blind and low-vision travellers. Current datasets rarely contain collision examples or temporal context, forcing most AI pipelines to rely on generic scene-understanding tasks whose output is too high-dimensional for haptic or auditory feedback. We created NavIndoor , a Unity-based, procedurally-generated maze environment that delivers paired RGB and semantic-segmentation streams together with collision-aware rewards. A head-mounted blind “digital twin” was trained end-to-end with reinforcement learning to estimate a four-way action/value vector. Training used 250 episodes with a progressive increase in difficulty and semantic mask augmentation. Performance was (i) benchmarked against human players in-sim; (ii) probed on five unseen houses from the Active Vision Dataset (AVD) via linear classifiers that predict forward-path safety thresholds; (iii) timed on a Jetson Orin Nano powered with a portable power supply; (iv) evaluated on an early-stage haptic prototype during a proof-of-concept trial with a single blindfolded participant. In-simulation the best model attained a mean reward of 74.3 (73 % of human score). On the Active Vision Dataset (AVD) it achieved an AUC of 0.92 for detecting safe forward pathways and outperformed larger self-supervised or supervised backbones. The estimated value function V θ ( s ) correlated monotonically with the walkable distance, validating its interpretation as a one-dimensional safety signal. The full pipeline ran between 7 and 16 frames per second (FPS) on the Jetson with an end-to-end (image-to-motor) latency of 1.1 s. The model’s outputs were able to guide the blindfolded participant to navigate without collisions in a 38-meter curved corridor. Training on procedurally-generated semantic maps yields compact RL policies whose safety cues transfer directly to real indoor scenes without photorealistic rendering or domain adaptation. The approach enables portable, low-power SSD prototypes and lays the groundwork for forthcoming clinical validation of safe, real-time navigation aids for the visually impaired. • The limited availability of navigation data for visually impaired individuals restricts the development of AI-driven assistive technologies. • NavIndoor is a new software tool that generates procedurally created mazes, enabling the rapid extraction of synthetic, human-like navigation data. • Synthetic virtual-environment data, combined with reinforcement learning, enables real-time extraction of low-dimensional cues for safe pathway identification.
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.
Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information Laura E. Marin, Daniel I. Zavaleta-Guzman, Jessyca I. Gutierrez-Garcia, Daniel Racoceanu, Fanny L. Casado Discover Oncology, 2025 Biopsy information and protein Prostate-Specific Antigen (PSA) levels are the most robust information available to oncologists worldwide to diagnose and decide therapies for prostate cancer patients. However, prostate cancer presents a high risk of recurrence, and the technologies used to evaluate it demand more complex resources. This paper aims to predict Biochemical Recurrence (BCR) based on Whole Slide Images (WSI) of biopsies, Gleason scores, and PSA levels. A U-net model was used to segment phenotypic features and trained on images from the Prostate Cancer Grade Assessment (PANDA) database to segment tumorous regions from pre-processed and scored WSI of biopsies. Then, the model was tested on data from publicly available repositories achieving an Intersection over Union of 87%. Tissue features, Gleason scores, and PSA levels provided high accuracy and precision in classifying patients according to their risk of presenting recurrence, for any Gleason score sampled. The trained classifier model demonstrated a 79.2% relative accuracy, and a precision of 69.7% for patients experiencing recurrences before 24 months. Our results provide a robust, cost-efficient approach using already available information to predict the risk of BCR.
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, Daniel Racoceanu 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, Daniel Racoceanu 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.
Visual Prostheses in the Era of Artificial Intelligence Technology Ilias Sarbout, Ayse Gungor, Mehdi Ounissi, Samy Zaher, Maurice Ptito, Ron Kupers, Daniel Racoceanu, Dan Milea Eye and Brain, 2025 Background: Over the past few decades, technological advancements have transformed invasive visual prostheses from theoretical concepts into real-world applications. However, functional outcomes remain limited, especially in visual acuity. This review aims to summarize current developments in retinal and cortical prostheses (RCPs) and critically assess the role of artificial intelligence (AI) in advancing these systems. Purpose: To describe current RCPs and provide a systematic review on image and signal processing algorithms designed for improved clinical outcomes. Patients and Methods: We performed a systematic review of the literature related to AI subserving prosthetic vision, using mainly PubMed, but also, Elicit, a dedicated AI-based reference research assistant. A total of 455 studies were screened on PubMed, of which 23 were retained for inclusion. An additional 5 studies were identified and included through Elicit. Results: The analysis of current RCPs highlights various limitations affecting the quality of the visual flow provided by current artificial vision. Indeed, the 28 reviewed studies on AI covered two applications for RCPs including extraction of saliency in camera captured images, and consistency between electrical stimulation and perceived phosphenes. A total of 14 out of 28 studies involved the use of artificial neural networks, of which 12 included model training. Evaluation with data from a visual prosthesis was conducted in 7 studies, including 1 that was prospectively assessed with a human RCP. Validation with empirical data from human or animal data was performed in 22 out of 28 studies. Out of these, 15 were validated using simulated prosthetic vision. Finally, out of 22 studies leveraging a mathematical model for phosphenes perception, 14 used a symmetrical oversimplified modeling. Conclusion: AI algorithms show promise in optimizing prosthetic vision, particularly through enhanced image saliency extraction and stimulation strategies. However, most current studies are based on simulations. Further development and validation in real-world settings, especially through clinical testing with blind patients, are essential to assess their true effectiveness.
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, Cristina Hobeanu, Philippe Gohier, Catherine Vignal‐Clermont, Oana M. Dumitrascu, Salomon‐Yves Cohen, Wolf A. Lagrèze, Nicolas Feltgen, Frank van der Heide, Cédric Lamirel, Jost B. Jonas, Michael Obadia, Daniel Racoceanu, Dan Milea 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, Anuradha Kar, Daniel Racoceanu, Benoît Delatour, Lev Stimmer 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.
Efficient 3D reconstruction of Whole Slide Images in Melanoma Janan Arslan, Mehdi Ounissi, Haocheng Luo, Matthieu Lacroix, Pierrick Dupré, Pawan Kumar, Arran Hodgkinson, Sarah Dandou, Romain Larive, Christine Pignodel, Laurent L. Cam, Ovidiu Radulescu, Daniel Racoceanu Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2023
Preface Jean-Rémi Lapaire Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M. van der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, Oscar Geessink, Nikolaos Stathonikos, Marcory CRF van Dijk, Peter Bult, Francisco Beca, Andrew H Beck, Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, Aoxiao Zhong, Qi Dou, Quanzheng Li, Hao Chen, Huang-Jing Lin, Pheng-Ann Heng, Christian Haß, Elia Bruni, Quincy Wong, Ugur Halici, Mustafa Ümit Öner, Rengul Cetin-Atalay, Matt Berseth, Vitali Khvatkov, Alexei Vylegzhanin, Oren Kraus, Muhammad Shaban, Nasir Rajpoot, Ruqayya Awan, Korsuk Sirinukunwattana, Talha Qaiser, Yee-Wah Tsang, David Tellez, Jonas Annuscheit, Peter Hufnagl, Mira Valkonen, Kimmo Kartasalo, Leena Latonen, Pekka Ruusuvuori, Kaisa Liimatainen, Shadi Albarqouni, Bharti Mungal, Ami George, Stefanie Demirci, Nassir Navab, Seiryo Watanabe, Shigeto Seno, Yoichi Takenaka, Hideo Matsuda, Hady Ahmady Phoulady, Vassili Kovalev, Alexander Kalinovsky, Vitali Liauchuk, Gloria Bueno, M. Milagro Fernandez-Carrobles, Ismael Serrano, Oscar Deniz, Daniel Racoceanu, Rui Venâncio, and JAMA Journal of the American Medical Association, 2017
Preface Daniel Racoceanu, Peter Hufnagl Computerized Medical Imaging and Graphics, 2017
Resource-Centered Distributed Processing of Large Histopathology Images Daniel Salas, Jens Gustedt, Daniel Racoceanu, Isabelle Perseil Proceedings 19th IEEE International Conference on Computational Science and Engineering 14th IEEE International Conference on Embedded and Ubiquitous Computing and 15th International Symposium on Distributed Computing and Applications to Business Engineering and Science Cse Euc Dcabes 2016, 2017
Gland segmentation in colon histology images: The glas challenge contest Korsuk Sirinukunwattana, Josien P.W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R.J. Snead, Nasir M. Rajpoot Medical Image Analysis, 2017
Statistically representative cloud of particles for crowd flow tracking Patrick Jamet, Stephen Chai Kheh Chew, Antoine Fagette, Jean-Yves Dufour, Daniel Racoceanu Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2015
A stochastic model for automatic extraction of 3D neuronal morphology Sreetama Basu, Maria Kulikova, Elena Zhizhina, Wei Tsang Ooi, Daniel Racoceanu Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Consciousness-driven model for visual attention Pierre Cagnac, Noel Di Noia, Chao-Hui Huang, Daniel Racoceanu, Laurent Chaudron Proceedings of the International Joint Conference on Neural Networks, 2011
Automatic area classification in peripheral blood smears Wei Xiong, Sim-Heng Ong, Joo-Hwee Lim, Kelvin Weng Chiong Foong, Jiang Liu, Daniel Racoceanu, Alvin G. L. Chong, Kevin S. W. Tan IEEE Transactions on Biomedical Engineering, 2010
Advanced methods for recurrent hierarchical systems modeling. Application to producer-consumer distributed energy production systems Proceedings of 2009 7th Asian Control Conference Ascc 2009, 2009
Spatial relationships over sparse representations Nicolas Lomenie, Daniel Racoceanu 2009 24th International Conference Image and Vision Computing New Zealand Ivcnz 2009 Conference Proceedings, 2009
Emergent behavior control patterns in robotic collectives Razvan-Dorel Cioarga, Mihai V. Micea, Vladimir Cretu, Daniel Racoceanu Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Boosting the performances of the recurrent neural network by the fuzzy min-max Romanian Journal of Information Science and Technology, 2009
Automatic breast cancer grading of histopathological images Jean-Romain Dalle, Wee Kheng Leow, Daniel Racoceanu, Adina Eunice Tutac, Thomas C. Putti Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 08 Personalized Healthcare Through Technology, 2008
Knowledge-guided semantic indexing of breast cancer histopathology images Adina Eunice Tutac, Daniel Racoceanu, Thomas Putti, Wei Xiong, Wee-Kheng Leow, Vladimir Cretu Biomedical Engineering and Informatics New Development and the Future Proceedings of the 1st International Conference on Biomedical Engineering and Informatics Bmei 2008, 2008
Inter-media concept-based medical image indexing and retrieval with UMLS at IPAL Caroline Lacoste, Jean-Pierre Chevallet, Joo-Hwee Lim, Diem Thi Hoang Le, Wei Xiong, Daniel Racoceanu, Roxana Teodorescu, Nicolas Vuillenemot Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
Stripe: Image feature based on a new grid method and its application in ImageCLEF Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006
Monitoring approach using recurrent radial basis function neural networks and neuro-fuzzy systems Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings Icnnb 05, 2005
Overview on diagnosis methods using artificial intelligence application of fuzzy petri nets 2004 IEEE Conference on Robotics Automation and Mechatronics, 2004
From the spherical to an elliptic form of the dynamic RBF neural network influence field Proceedings of the International Joint Conference on Neural Networks, 2002
The RRBF dynamic representation of time in radial basis function network IEEE International Conference on Emerging Technologies and Factory Automation ETFA, 2001
Use of an homographic transformation jointly to the singular perturbation for the resolution of Markov chains. Application to the operational safety study Proceedings IEEE International Conference on Robotics and Automation, 1994
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
