2024Activity reportProject-TeamOPIS

4.1.1 Design of robust neural networks in safety critical industrial domains

Participants: Ana Neacsu , Kavya Gupta , Fragkiskos Malliaros , Jean-Christophe Pesquet (Collaboration: P. Combettes, North Caroline State University ; C. Burileanu, Polithenica University of Bucharest)

One of the main challenges faced today by companies like Thales or Schneider Electric designing advanced industrial systems, is to ensure the safety of new generations of products based on the use of neural networks. Since 2013, neural networks have been shown to be sensitive to adversarial perturbations. Deep neural networks can thus be fooled, in an intentional (security issue) or in undeliberate manner (safety issue), which raises a major robustness concern for safety-critical systems which need to be certified by an independent certification authority prior to any entry into production/operation. Tech- niques based on mathematical proofs of robustness are generally preferred by industrial safety experts since they enable a safe-by-design approach that is more efficient than a robustness verification activity done a posteriori with a necessarily bounded effort. Among the possible mathematical approaches, we focus on those relying upon the analysis of the Lipschitz properties of neural networks 67, 26. Such properties play a fundamental role in the understanding of the internal mechanisms governing these complex nonlinear systems. Besides, they make few assumptions on the type of non-linearities used and are thus valid for a wide range of networks.

4.1.2 Certification of segmentation networks

Participants: Othmane Laousy , Maria Vakalopoulou (Collaboration: G. Chassagnon and M.-P. Revel, Paris Cité University ; N. Paragios, Therapanacea ; A. Araujo, S. Garg and F. Khorram, New York University)

The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classification models, segmentation models can be vulnerable to adversarial perturbations, which hinders their use in critical-decision systems like healthcare or autonomous driving. Recently, randomized smoothing has been proposed to certify segmentation predictions by adding Gaussian noise to the input to obtain theoretical guarantees. However, this method exhibits a trade-off between the amount of added noise and the level of certification achieved. In this topic, we address the problem of certifying segmentation prediction using a combination of randomized smoothing and diffusion models. We challenge our methods in both general computer vision and medical imaging dataset.

4.2.1 Imaging radiomics and pathomics to assess response to treatment

4.2.2 Analysis of histopathology images for cancer treatment

Participants: Ségolène Martin , Nora Ouzir , Jean-Christophe Pesquet , Aymen Sardroui (Collaboration: A. Laurent-Bellue, C. Guettier: APHP, Hôpital du Kremlin-Bicêtre ; A. Beaufrère, K. Mondet, V. Paradis, APHP, Hôpital Beaujon)

The core focus of our research revolves around scrutinizing cancer through the utilization of digital slide images resulting from biopsies or surgical resection. Our exploration stands at the intersection of cutting-edge AI technology and its invaluable potential for advancing precision medicine, and more particularly liver cancer (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) diagnosis and treatment. The challenges to be solved are related to the limited number of available annotated data and the large-size of whole slide images (WSIs) 56, 57.

4.2.3 Vision, machine learning and precision medicine

Participants:Younes Belkouchi , Loïc Le Bescond , Hugues Talbot (Collaboration: N. Lassau, Institut Gustave Roussy)

In March 2020, the PRISM institute of Gustave-Roussy was launched. The aim of this project, funded for 5 years, is to develop targeted treatments that are more likely to work on specific patients.

The mission of this “second-generation” precision medicine centre will be to model cancer on an individual scale by creating numerical avatars of tumours. The aim is to identify patients with the most aggressive cancers very early in the disease, without waiting for relapses, in order to offer them the most appropriate treatment from the start of treatment, using the huge volume of clinical, biological and molecular data and their analysis by artificial intelligence. PRISM will conduct large-scale clinical studies and develop molecular analysis technologies and data analysis methods.

Coordinated by Professor Fabrice André, Research Director of Gustave Roussy, Inserm Research Director and Professor at Paris-Saclay University, Prism aims to revolutionize the understanding of the molecular and biological mechanisms of cancer development and progression through artificial intelligence. Based on increasingly rich data of various types (clinical, genomic, microbiological, imaging, etc.), learning algorithms make it possible to develop finer diagnostic and prognostic tools, and thus to propose therapies that are personalised according to the characteristics of the individual.

Funded by the French National Research Agency, PRISM received the IHU label in 2018, followed by the National Center for Precision Medicine label.

4.2.4 Physics-informed, generative models for heart left ventricle perfusion analysis

Participants: Raoul Salle de Chou, Hugues Talbot (Collaboration: I. Vignon-Clementel, SIMBIOTX Team Inria ; L. Najman, Université Gustave-Eiffel, L. Papamanolis Stanford university, USA, California)

Coronary arteries feed the heart muscles with nutrients and oxygen. As such, they are some of the most critical blood vessel in the entire body. Coronary disease is difficult to diagnose especially when it affects the smaller branches of these vessels, because direct imaging of these vessels is infeasible with current medical imaging technology. Instead, blood perfusion through the myocardium can be imaged and is correlated with both arterial and myocardium disease. However, perfusion imaging is challenging, invasive and expensive because it relies on radioactive tracers.

A previous model was developed for myocardial perfusion simulation for coronary artery disease in [link] to replace the actual exam with a numerical twin and conduct it via simulations. The model aims at reproducing ${[}^{15}O]H_{2}O$ PET imaging exam using only CT scans as input. The simulation is based on :

1. the detection, segmentation and simulation of blood flow through the coronary vessels visible on the injected CT scan;
2. a patient-specific method for generating small 3D vessels consistent with the vessels detected. The rules for the growth of these vessels are based on physiology and simulated blood flows;
3. a perfusion simulation model that considers the myocardium as a porous medium.

For this a linear Darcy model is used to simulate blood flow through the porous medium. However, in addition to a high computational cost, the simulation fails to accurately reproduce some diseases, particularly those that affect medium-size coronary branches.

The main goal of this project is to combine Machine Learning (ML) methods with physical simulations, in order to improve the current simulation pipeline. ML algorithms are used to learn from PET imaging exams while being guided by simulation hypothesis, thereby diminishing the dependency on patient data. To achieve this, each part of the simulation is to be replaced by an ML model. Following successful replication of simulation outcomes, the model will undergoes refinement using patient data.

A finite volume physics informed graph neural network was developed to solve the Darcy equations on irregular shapes serving as a substitute for the myocardium component in perfusion simulation. Preliminary results indicate superior performance of this model in terms of accuracy and generalization compared to classical ML approaches. In 58, we introduced a novel optimization framework for the generation of the synthetic small vessels utilizing the constructed constrained optimization (CCO) method. Our new approach simulated similar 2D vascular trees as the original CCO method in terms of morphometry while producing better optimal solutions at lower computational cost. This new approach is expected to be more readily reproducible using ML methods compared to the original CCO technique.

Additionally, work has been conducted towards the determination of the myocardium perfusion regions. Determining these regions, and their associated vessel is a crucial step in current simulation pipeline. However, the current calculation method is inaccurate and highly sensitive to the resolution of segmented vessels. A more robust and accurate model, employing graph neural networks (GNNs), has been developed for the determination of these regions.

4.3.1 Sparse signal processing in chemistry

Participants:Emilie Chouzenoux , Mouna Gharbi , Jean-Christophe Pesquet (Collaboration: L. Duval, IFPEN, Rueil Malmaison)

Peak-signal retrieval is a core challenge in separative analytical chemistry (AC). For instance, in chromatography, spectrometry, spectroscopy, peak localization, amplitude, width or area provide useful chemical quantitative information. We investigated this problem through the deep unrolling paradigm, in 43, 21, in collaboration with Dr. L. Duval, Research Engineer at IFP Energies Nouvelles, France.

4.3.2 Image restoration for multiphoton microscopy

Participants: Julien Adjenbaum, Emilie Chouzenoux , Jean-Baptiste Fest , Ségolène Martin , Jean-Christophe Pesquet (Collaboration: C. Lefort, XLIM, CNRS, Limoges ; M. Chalvidal, ANITI, Toulouse)

Through an ongoing collaboration with physicists from XLIM laboratory (CNRS, Limoges, France), we propose advanced mathematical and computational solutions for multiphoton microscopy (MPM) 3D image restoration. This modality enjoys many benefits such as a decrease in phototoxicity and increase in penetration depth. However, blur and noise issues can be more severe than with standard confocal images. Our objective in 51, 4 was to drastically improve the quality of the generated images and their resolution by improving the characterization of the PSF of the system and compensating its effect. We consider the application of the improved MPM imaging tool to the microscopic analysis of muscle ultrastructure and composition, with the aim to help diagnosing muscle disorders including rare and orphan muscle pathologies, and to visualize bacteria and viral structures.

4.3.3 Reconstruction approaches in PET imaging

Participants:Emilie Chouzenoux , Alix Chazottes , Jean-Christophe Pesquet , Claire Rossignol (Collaboration: F. Sureau, CEA)

Positron emission tomography (PET) is a quantitative functional imaging modality used to track the fate and/or dynamics of a radiotracerpreviously injected into a patient. This technique is particularly used in oncology for diagnosis and therapeutic monitoring, in the study ofneurodegenerative diseases, and in pharmacology. In dynamic PET, the temporal evolution of the spatial distribution of the radiotracerduring the examination is taken into account for the estimation of physiological parameters allowing for a fine characterization of themolecular mechanisms at play (receptor concentration, absorption, dissociation constants, binding potential, etc.). In the PhD thesis of Alix Chazottes , in collaboration between OPIS and CEA, we aim to propose methodological developments in the fields ofoptimization and learning to address the problem of robust dynamic PET reconstruction 40.

4.4.1 Geometric Graph Neural Networks for molecular and chemical systems

Participants:Alexandre Duval , Fragkiskos Malliaros (Collaboration: V. Schmidt, Alex Hernandez, Y. Bengio, David Rolnick, Mila - Quebec AI Institute ; S. Miret, Intel Labs Berkeley)

Graph Neural Networks (GNNs) currently constitute state-of-the-art models for solving prediction tasks on graphs. Through the flexible formulation of the message passing mechanism, GNNs can learn informative latent representations of graph entities at different resolution levels (e.g., node-, edge-, graph-level). In many practical applications in molecular and chemical systems, the nodes of the graph have associated geometric attributes (e.g., coordinates, velocities) related to their position in the 3D space. In this context, geometric graphs represent the interaction of atoms in the 3D space, encapsulating a range of physical symmetries such as rotations and translations. Existing GNN models often overlook this aspect, rendering them ill-suited for prediction tasks on geometric graphs. Recently, Geometric GNN architectures tailored to respect physical symmetries have emerged as flexible models of atomic systems. Through an ongoing collaboration with Mila - Quebec AI Institute, Université de Montréal, McGill University, and Intel Labs, we study geometric GNN models, focusing both on design principles as well as on practical applications in materials modeling (e.g., property prediction and molecule generation).

4.4.2 Graph inference for gene regulation analysis

Participants:Fragkiskos Malliaros (Collaboration: A. Pirayre, F. Bidard, and L. Duval, IFPEN)

The discovery of novel gene regulatory processes improves the understanding of cell phenotypic responses to external stimuli for many biological applications, such as medicine, environment or biotechnologies. To this purpose, transcriptomic data are generated and analyzed from DNA microarrays or more recently RNAseq experiments. They consist in genetic expression level sequences obtained for all genes of a studied organism placed in different living conditions. From these data, gene regulation mechanisms can be recovered by revealing topological links encoded in graphs. In regulatory graphs, nodes correspond to genes. A link between two nodes is identified if a regulation relationship exists between the two corresponding genes. In our work, we propose to address this network inference problem with recently developed techniques pertaining to graph optimization. Given all the pairwise gene regulation information available, we propose to determine the presence of edges in the considered GRN by adopting an energy optimization formulation integrating additional constraints. Either biological (information about gene interactions) or structural (information about node connectivity) a priori are considered to restrict the space of possible solutions. Different priors lead to different properties of the global cost function, for which various optimization strategies, either discrete and continuous, can be applied.

4.4.3 Graph machine learning for spatiotemporal data

Participants:Fragkiskos Malliaros (Collaboration: J. Castro-Correa, Mohsen Badiey, Univ. of Delaware ; J. H. Giraldo, Télécom Paris ;A. Mondal, Univ. of Surrey ; T. Bouwmans, La Rochelle Univ.)

Numerous real-world prediction problems involve spatiotemporal data. For example, consider sensors scattered across diverse geographical regions measuring environmental conditions (e.g., temperature, pollution) or functional magnetic resonance imaging (fMRI) data capturing brain activity. Both scenarios generate data inherently rich in spatiotemporal structure, benefiting from the relational inductive bias of graph-based modeling. In an ongoing collaboration with the University of Delaware, Télécom Paris, and La Rochelle Université, we have introduced a methodology that leverages graph-based modeling, enabling time series imputation with GNNs. Major challenges here concern inducing temporal and relational smoothness assumptions into the model as well as inferring the (often unknown) graph structure. Furthermore, an intriguing aspect involves enhancing spatiotemporal graph models with causal properties to capture causal influence effects among entities.

4.4.4 Graph representation learning for computational medicine

Participants:Emilie Chouzenoux , Fragkiskos Malliaros (Collaboration: A. Majumdar, IIIT Delhi ; G. Chierchia, ESIEE Paris ; B. Liu, D. Papadopoulos, G. Tsoumakas, A. Papadopoulos, Aristotle Univ. of Thessaloniki )

Following the Associate International Inria Team COMPASS (ended in 2023), led by Emilie Chouzenoux and Dr. A. Majumdar (IIIT Delhi), we pursue our research to investigate new models and inference tools to understand and predict optimal drug association, so as to tackle real-life problems of computational drug discovery. We introduced graph-based regularization techniques in order to incorporate expert knowledge and metadata in matrix completion tasks arising in the prediction of drug-virus and drug-drug associations. Our recent works apply the proposed methodology to drug repositioning 66.

The discovery of drug-target interactions is also explored by Fragkiskos Malliaros , in collaboration with Aristotle University of Thessaloniki. Accurately identifying reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. We have considered multi-layered network modeling to handle diverse drug and target similarities, introducing an optimization framework called Multiple similarity DeepWalk-based Matrix Factorization (MDMF) for DTI prediction. Current efforts in this direction involve leveraging Graph Neural Networks and self-supervised learning tools.

4.4.5 Graph inference for time series analysis in earth and climate applications

Participants:Emilie Chouzenoux (Collaboration: V. Elvira, Univ. Edinburgh, UK)

Studying the causal inter-dependencies in complex dynamical systems is a critical challenge. We explore sparse graphical models to gain insight through observational causal discovery. We revisit Granger causality under a graphical perspective of state-space models. We investigate expectation-maximisation algorithms 3814 for estimating matrix parameters in the state equation of a linear-Gaussian state-space model under sparse priors, emphasizing both causal and correlation relationships among time series samples.

4.5.1 Artificial intelligence for accessible eccentric photorefraction

Participants: Emilie Chouzenoux , Nguyen Vu , Jean-Christophe Pesquet , Clement Lavaud (Collaboration: P. Pinault, S. Boutinon, M. Peloux, R&D ESSILOR)

Affordable and accessible screening tools are crucial to diagnose and, hence, effectively address vision impairment. In collaboration with ESSILOR R&D, we developped novel pipelines based on photorefraction and advanced AI techniques which complies with these requirements 62. Our results pave the way for a practical application of the proposed device for an accurate vision loss diagnostic in a real setting.

4.5.2 Imaging biomarkers and characterization for chronic lung diseases

Participants: Othmane Laousy , Maria Vakalopoulou (Collaboration: S. Christodoulidis, G. Chassagnon, M.-P. Revel, APHP ; N. Paragios, Therapanacea)

Diagnosis and staging of lung diseases is a major challenge for both patient care and approval of new treatments. Among imaging techniques, computed tomography (CT) is the gold standard for in vivo morphological assessment of lung anatomy currently offering the highest spatial resolution in lung diseases. Although CT is widely used its optimal use in clinical practice and as an endpoint in clinical trials remains controversial. Our goal in the PhD thesis of Othmane Laousy , is to provide automatic and accurate tools that could help clinicians with their everyday practice.

4.5.3 AI for small bowel obstruction diagnosis

Participants:Alix Chazottes , Emilie Chouzenoux , Maxence Gélard, Jean-Christophe Pesquet (Collaboration: Q. Vanderbecq, M. Zins, Hôpital Saint Joseph ; M. Wagner, LIB, Sorbonne Univ.)

Small bowel obstruction (SBO) is a common nontraumatic surgical emergency. All guidelines recommend computed tomography (CT) as the first-line imaging technique for patients with suspected mechanical SBO with a four-fold goal: (i) to confirm or refute the diagnosis of SBO and, when SBO is present, (ii) to locate the site of the obstruction, that is, the transition zone (iii) to identify the cause, and (iv) to look for complications such as strangulation or perforation. Identifying SBO and differentiating its causes (e.g., open-loop and closed-loop mechanisms) is time-consuming and subject to inter-observer and intra-observer variability.

The aim of this collaborative project between Inria Saclay OPIS, Hôpital St Joseph, and LIB, Sorbonne University, is to investigate AI approaches for a guided SBO diagnosis from 3D CT scans 36.

4.5.4 A generative model for heart left ventricle perfusion analysis

Participant:Hugues Talbot (Collaboration: L. Najman, ESIEE Paris ; I. Vignon-Clementel, REO Team leader, Inria ; C. Taylor, Heartflow Inc.)

Cardio-vascular diseases continue to be the leading cause of mortality in the world. Understanding these diseases is a current, challenging and essential research project. The leading cause of heart malfunction are stenoses causing ischemia in the coronary vessels. Current CT and MRI technology can assess coronary diseases but are typically invasive, requiring risky catheterization and renal-toxic contrast agents injection. In collaboration with the REO team headed by Irène Vignon-Clementel, and Heartflow, a US based company, we have in the past contributed to Heartflow's major product, that replaces these physical exams with image-based exams only, limiting the use of contrast agents and in the cases that do not require a stent insertion, eliminating catheterisation. Heartflow is current the market leader in non-invasive coronary exams and the owner of most of the relevant IP in this domain.

Unfortunately, current imaging technology is unable to assess coronary disease along the full length of coronary vessels. CT is limited to a resolution of about 1mm, whereas coronary vessels can be much smaller, down to about 10 micrometers in diameter. To assess blood vessel health down to the smallest sizes, blood perfusion imaging technique throughout the heart muscle must be used instead. Perfusion imaging with PET or a Gamma camera, the current gold standard, is an invasive technology requiring the use of radioactive tracers. To avoid using these, a lower quality estimate of perfusion can be achieved using some ToF or injected gated MRI modalities.

We have investigated patient-specific vessel generation models together with porous model simulations in order to propose a direct model of perfusion imaging, based on the known patient data, computer flow dynamic simulations as well as experimental data consistent with known vessel and heart muscle physiology. The objective of this work is to both to provide a useful, complex forward model of perfusion image generation, and to solve the inverse problem of locating and assessing coronary diseases given a perfusion exam, even though the affected vessels may be too small to be imaged directly.

Continuing on our work from the period 2015-2019, this year we proposed a functional myocardial perfusion model consisting of the CT-derived segmented coronary vessels, a simulated vessel tree consisting of several thousands of terminal vessels, filling the myocardium in a patient-specific way, consistent with physiology data, physics-based and empirically-observed vessel growth rules, and a porous medium. We produced and validated a CFD code capable of simulating blood flow in all three coupled compartments, which allows us to simulate perfusion realistically.

7.1.1 Prox Repository

Web site: Prox Repository

• Software Family: utility
• Audience: universe
• Evolution and maintenance: long term support
• Context/Role of OPIS: This website was created by E. Chouzenoux and J.-C. Pesquet from OPIS, along with P.L. Combettes, North Carolina State University, and G. Chierchia, ESIEE Paris. The maintenance is made by summer interns funded by OPIS, and by the authors of the website.
• Duration of the Development: The website was released in 2016, and is maintained regularly since then.
• Proximity operators have become increasingly important tools as basic building blocks of proximal splitting algorithms, a class of algorithms that decompose complex composite convex optimization methods into simple steps involving one of the functions present in the model. This website provides formulas for efficiently computing the proximity operator of various functions, along with the associated codes in Matlab/Python langages.
• The codes provided are distributed under the licence CeCill-B.

7.1.2 The PINK image library

Web site: PINK

• Software Family: utility
• Audience: universe
• Evolution and maintenance: long term support
• Context/Role of OPIS: H. Talbot is among the creators of this library and is still actively involved in its maintenance.
• Duration of the Development: This software has been developed and maintained since 2011.
• The PINK image library is a general-purpose, open-source, portable image processing library specializing in discrete geometry and mathematical morphology. It is the result of several decades of research in these domains and features state-of-the art algorithmic implementation of both classical and leading edge DG and MM operators. These include nD parallel thinning and skeletonization methods and efficient hierarchical morphological transforms.
• This code is distributed under the CeCILL license.

7.1.3 The Vivabrain AngioTK toolkit

Web site: Vivabrain AngioTK toolkit

• Software Family: vehicle
• Audience: partners
• Evolution and maintenance: basic
• Context/Role of OPIS: H. Talbot participated to the programming of this software.
• Duration of the Development: This software has been released in 2018.
• AngioTK is a toolkit supported by Kitware (the authors of VTK) for the filtering, segmentation, generation and simulation of blood vessels. It was started in the context of the Vivabrain ANR project in 2012, but continues with the same as well as new partners. Applications are numerous, from the simulation and understanding of perfusion (see associated theme) to the simulation of realistic blood flow MRI images with associated ground truth, via the generation of blood vessel atlases.
• This code is distributed under the Apache License, Version 2.0.

7.1.4 A scientific image viewer

Web site: imview

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: H. Talbot is the author of this software.
• Duration of the Development: This software has been released in 2001. His last version has been updated in 2014.
• This general-purpose and cross-platform scientific image viewing tool has been part of the Debian Linux distribution since 2001. This viewer is well adapted to display 2D with high-precision data images (floating-point, etc), as well as 3D and hyper-spectral data. It features an interactive segmentation tool for multispectral data and is scriptable.
• The codes provided are distributed under a GNU General Public License version 2.0 (GPLv2).

7.1.5 TCGA segmentation

Web site: TCGA segmentation

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code was produced by the OPIS PhD student M. Le Rousseau.
• Duration of the Development: This software was developed during the PhD thesis of M. Le Rousseau, mostly during the year 2020.
• This is a Python/Pytorch code that implements an end-to-end Whole Slide Imaging pre-processing pipeline from The Cancer Genome Atlas download documents, as well as a complete implementation of deep learning tumor segmentation from WSI binary labels.
• The codes provided are distributed under a GNU Affero General Public License v3.0.

7.1.6 ScanCovIA

Web site: ScanCovIA

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This software is the product of the ScanCovIA collaborative project between OPIS, IGR, CentraleSupélec and the start-up Owkin. Several PhD students of OPIS were involved in the programming of this software.
• Duration of the Development: This software was developed during the year 2020.
• This is a Python/Pytorch code allowing to reproduce the results of the ScanCovIA project.
• The codes provided are distributed under a GPL v3.0 license.

7.1.7 Graphical inference in linear-Gaussian state-space models

Web site: Graphical inference in linear-Gaussian state-space models

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: The code is produced by OPIS member Emilie Chouzenoux .
• Duration of the Development: This software has been developped from 2021. The most recent release is from July 2023.
• This is a set of Matlab codes, to reproduce the experimental results from the article 14.
• The codes provided are distributed under CeCILL-B V1 license.

7.1.8 Antibiotic bacteria interaction: dataset and benchmarking

Web site: Antibiotic bacteria interaction: dataset and benchmarking

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: The code was produced by S. Chatterjee, PhD student at IIIT Delhi, in the context of the Inria International Associate Team COMPASS.
• Duration of the Development: This software has been developped during the year 2023.
• This is a database, and a set of Python codes, to reproduce the experimental results from the article 66.
• The codes provided are distributed through Github platform, under a General Public License v3.0.

7.1.9 Joint registration tumor segmentation

Web site: Joint registration tumor segmentation

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code was produced by the OPIS PhD student T. Estienne.
• Duration of the Development: This software was developed during the PhD thesis of T. Estienne, mostly during the year 2019.
• This is a Python/Pytorch code that allows to reproduce the results of the paper [link].
• The codes provided are distributed under a General Public License v3.0.

7.1.10 U-HQ

Web site: U-HQ

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code was produced by the OPIS PhD student Mouna Gharbi .
• Duration of the Development: This software was developed during the PhD thesis of M. Gharbi, mostly during the year 2023.
• This is a Python/Pytorch code allowing to reproduce the results of the paper 21.
• The codes provided are distributed under a GNU General Public License v3.0.

7.1.11 CITRUS

Web site: CITRUS

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Duration of the Development: This software is developed as part of a collaboration with Télécom Paris, in the context of the postdoctoral position of A. Einizade (co-supervised by F. Malliaros and J. Giraldo).
• This is a Python code allowing to reproduce the results of the paper 41.
• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.12 Missing Modalities in Multimodal Recommendations

Web site: GitHub link

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Duration of the Development: This software is developed in the context of the postdoctoral position of D. Malitesta, in collaboration with Politecnico di Bari (Italy).
• This is a Python code allowing to reproduce the results of the paper 50.
• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.13 Graph-based Uplift Modeling

Web site: UMGNet

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Duration of the Development: This software is developed in the context of a collaboration with the University of Luxembourg (G. Panagopoulos and J. Pang).
• This is a Python code allowing to reproduce the results of the paper 55.
• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.14 GegenGNN

Web site: GegenGNN

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Duration of the Development: This software is developed in the context of a collaboration with the University of Delaware (J Castro-Correa and M. Badiey) and Télécom Paris (J. Giraldo).
• This is a Python code allowing to reproduce the results of the paper 11.
• The codes provided are distributed under the licence GNU General Public License v3.0.

10.1.1 STIC/MATH/CLIMAT AmSud projects

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

10.3.1 H2020 projects

10.4.1 ANR

• Program: Chaire IA - ANR
  • Project acronym: Bridgeable
  • Project title: BRIDinG thE gAp Between iterative proximaL methods and nEural networks
  • Duration: 2020-2024
  • Coordinator: Jean-Christophe Pesquet

• Program: ANR PRCE
  • Project acronym: R-Vessel-X
  • Project title: Extraction et interprétation robustes des réseaux vasculaires dans les images biomédicales hépatiques
  • Duration: 2018-2022
  • Coordinator: A. Vacavant (Univ. Clermont Auvergne), local: Hugues Talbot

• Program: ANR RHU
  • Project acronym: Innov4-ePiK
  • Project title: Innovative diagnostic and therapeutic approaches in epileptic and developmental encephalopathies linked to potassium channel genes using the 4P framework for medicine.
  • Duration: 2024-2028
  • Coordinator: Rima Nabbout, Université Paris Cité / AP-HP, local: Fragkiskos Malliaros

• Program: ANR JCJC
  • Project acronym: Hagnodice
  • Project title: Holistic explainable artificial intelligence schemes for lung cancer prognosis
  • Duration: 2022-2026
  • Coordinator: Maria Vakalopoulou

• Program: ANR JCJC
  • Project acronym: GraphIA
  • Project title: Scalable and robust representation learning on graphs
  • Duration: 2021-2025
  • Coordinator: Fragkiskos Malliaros

10.4.2 Others

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Emilie Chouzenoux : Keynote talk, journées des doctorants de l'université Paris Saclay, June 2024, Gif-sur-Yvette.
• Emilie Chouzenoux : Invited talk, workshop Approximate Inference in Theory and Practice, June 2024, Paris.
• Fragkiskos Malliaros : Kenote speaker, Graduate School of Translational Bioinformatics, Université Paris-Cite, France, 2024.
• Fragkiskos Malliaros : Invited talk, LIPN, Université Sorbonne Paris Nord, France, 2024.
• Antonio Silveti-Falls : Invited talk at the workshop Fondements Mathématiques de l'IA, 2024, Paris.
• Antonio Silveti-Falls : Invited talk at the European Conference on Operational Research, 2024, Copenhagen, Denmark.
• Antonio Silveti-Falls : Invited talk at the conference Optimization Techniques for Inverse Problems, 2024, Modena, Italy.
• Antonio Silveti-Falls : Invited talk at the TraDE-OPT Final Conference, 2024, Sestri Levante, Italy.
• Antonio Silveti-Falls : Invited talk at the workshop ISMP, 2024, Montreal, Canada.
• Jean-Christophe Pesquet : Invited speaker at the workshop Imaging inverse problems and generating models at the International Center for Mathematical Sciences, Edinburgh, April 2024.
• Jean-Christophe Pesquet : Invited lecturer at Ph.D international school in Mathematics and Machine Learning for Image Processing, Bologna, June 2024.
• Hugues Talbot : Invited talk on Non-linearly constrained neural-networks for inverse problems, NAIST, Nara, Japan, February 2024
• Hugues Talbot : Invited talk on AI for computer vision and medical imaging, Joint Robotics Lab, Tsukuba, Japan, February 2024
• Hugues Talbot : Invited talk on Digital Twin of the Left Ventricle, MICS seminar, CentraleSupelec, May 2024
• Thomas Guilmeau : Invited talk, 21st EUROPT Conference on Advances in Continuous Optimization (EUROPT 2024), June, Lund, Sweden.
• Loïc Le Bescond : Invited talk, Breast and Gynecological Cancer Conference (PGCC), Kinshasa, July 2024.

11.1.5 Leadership within the scientific community

• Emilie Chouzenoux : Senior member of the IEEE. Elected member of the EURASIP Signal and Data Analytics for Machine Learning TAC. Elected member of the Signal Processing Theory and Methods IEEE Technical Comittee.
• Mounir Kaaniche : Elected member of the EURASIP Technical Area Committee (TAC) on Signal and Data Analytics for Machine Learning (SiG-DML)
• Mounir Kaaniche : Senior member of the IEEE.
• Nora Ouzir : Elected member of the EURASIP Biomedical Image and Signals Analytics TAC.
• Jean-Christophe Pesquet : Senior honorary member of the Institut Universitaire de France and Fellow of IEEE and EURASIP.

11.1.6 Scientific expertise

The members of the team participated to numerous PhD Thesis Committees, PhD “comité de suivi individuel”, HdR Committees, recruiting Committees, and served as Grant Reviewers.

• Emilie Chouzenoux is member of the scientific committee of PIQ program of Inria.
• Emilie Chouzenoux is member of the executive committee of DataIA institute of University Paris Saclay.
• Emilie Chouzenoux is member of the scientific committee of the programme Mathématiques du Calcul Scientifique et de l'Ingénierie of the Labex Mathématiques Hadamard (Maths CSI LJH).
• Alix Chazottes is a student member of the ANR jury, for the section "Compétences and Métiers d'Avenir, plan de relance France2030".
• Mounir Kaaniche : Elected member of the expert committee of the national board of universities (CE61) at USPN.
• Nora Ouzir is an elected member of CCUPS, Consultative Commission of Paris-Saclay University.
• Maria Vakalopoulou served as a monitor-expert for EU research grants.

11.1.7 Research administration

• Jean-Christophe Pesquet is the head of the laboratoire Centre pour la Vision Numérique (CVN), CentraleSupélec.

11.1.8 Teaching administration

• Emilie Chouzenoux : attached professor in AI in CentraleSupélec, since sep. 2022.
• Nora Ouzir : Co-coordinator of the Bachelor in Artificial Intelligence, Data and Management Sciences, AIDAMS, with ESSEC Business School and CentraleSupélec.
• Nora Ouzir : Co-coordinator of the Thematic Sequence 2 (ST2) Viral Propagation, 1st year of CentraleSupélec.
• Nora Ouzir : Head for the Machine Learning Elective course in 2nd year of CentraleSupélec (210 students).
• Fragkiskos Malliaros : co-director of the Master in Data Sciences and Business Analytics (DSBA).
• Fragkiskos Malliaros : head of the Data and Information Sciences (SDI) specialization at CentraleSupélec.
• Jean-Christophe Pesquet : local head for the Optimization M.Sc. at CentraleSupélec.
• Jean-Christophe Pesquet : head for the main Optimization course in 2nd year of CentraleSupélec (600 students).
• Hugues Talbot : head of the Mastère Specialisé in "Trustworthy AI". It was in development since 2022 with IRT SystemX and CentraleSupelec Exed. This Master is designed for professionals who want to retrain in AI and focuses on the explainability and trustworthiness of models. It represents 400h of teaching and opened its first batch this year.
• Hugues Talbot : local head for CentraleSupélec the Mathematiques, Vision Apprentissage (MVA) Master of Ecole Normale Supérieure Paris-Saclay

11.2.1 Teaching

Several permanent members of OPIS were involved as lecturer (lec.) or lab instructors (lab), in the following courses.

• Master: Emilie Chouzenoux . Foundations of Distributed and Large Scale Computing, 26h (lec.), 3rd year CentraleSupélec and M.Sc. MVA Paris Saclay.
• Master: Emilie Chouzenoux . Advanced Machine Learning, 18h (lec.), 3rd year CentraleSupélec.
• Bachelor: Emilie Chouzenoux . Optimization, 24h (lec.), 1st year Bachelor AIDAMS, Univ. Paris Saclay.
• Master: Nora Ouzir. Machine Learning, 30h (lec.), 2nd year course, CentraleSupélec, EN
• Master: Nora Ouzir . Advanced Machine Learning, 24h (lec.), Msc DSBA, CentraleSupélec/ESSEC.
• Master: Nora Ouzir . Optimization, 15h (lab.), 2nd year course, CentraleSupélec.
• Bachelor: Nora Ouzir . Signal Procesing, 10h (lab.), 1st year course, CentraleSupélec.
• Master: Nora Ouzir . Medical Image Processing 3h (lec.), AI and Global Health program of the European University Alliance for Global Health (EUGLOH).
• Master: Fragkiskos Malliaros . Machine Learning in Network Science, 27h (lec.), Master in Data Sciences and Business Analytics, CentraleSupélec and ESSEC Business School, M.Sc. in Artificial Intelligence, CentraleSupélec, 3rd year data science mention, CentraleSupélec.
• Master: Fragkiskos Malliaros . Foundations of Machine Learning, 48h (lec.), Master in Data Sciences and Business Analytics, CentraleSupélec and ESSEC Business School.
• Master: Jean-Christophe Pesquet . Introductory course on Optimization, 33h (lec.), 2nd year CentraleSupélec.
• Master: Jean-Christophe Pesquet . Introduction to Optimization, 6h (lec.), M.Sc. MVA Paris Saclay.
• Master: Jean-Christophe Pesquet . Advanced course on Optimization, 10h (lec.), M.Sc. in Signal Processing and Automatic Control, Univ. Paris-Saclay.
• Master: Jean-Christophe Pesquet . Convex Optimization Algorithms, 15h (lec.), M.Sc. in Optimization, Univ. Paris-Saclay.
• Master: Antonio Silveti-Falls . Convergence, Integration, and Probability, 18h (lec.) + 18h (lab), 1st year course, CentraleSupélec.
• Master: Antonio Silveti-Falls . Optimization for Computer Vision, 21h (lec. + lab), 3rd year course, CentraleSupélec.
• Master: Antonio Silveti-Falls . Partial Differential Equations, 13.5h (lab), 1st year course, CentraleSupélec.
• Master: Antonio Silveti-Falls . Optimization, 12.5h (lab), 2nd year course, CentraleSupélec.
• Bachelor: Antonio Silveti-Falls . Analysis I, 18h (lab), First year Bachelors of Global Engineering students, Univ. Paris Saclay.
• Bachelor: Antonio Silveti-Falls . Analysis II, 18h (lab), First year Bachelors of Global Engineering students, Univ. Paris Saclay.
• Bachelor: Antonio Silveti-Falls . Analysis III, 18h (lab), First year Bachelors of Global Engineering students, Univ. Paris Saclay.
• Bachelor: Antonio Silveti-Falls . Probability, 18h (lab), First year Bachelors of Global Engineering students, Univ. Paris Saclay.
• Bachelor: Antonio Silveti-Falls . Topology and Functional Analysis, 18h (lab), Second year Bachelors of Global Engineering students, Univ. Paris Saclay.
• Master: Hugues Talbot . Convergence Integration Probabilité, 18h (lab), 1st year course, CentraleSupélec.
• Master: Hugues Talbot . High-performance computing, 12h (lab), 2nd year CentraleSupélec.
• Master: Hugues Talbot . Introduction à la morphologie mathématique: 12h (lab), 3rd year CentraleSupélec.
• Master: Hugues Talbot . Modern mathematical morphology (20h lec. + 8h lab), 3rd Year CentraleSupélec and MVA (ENS Paris-Saclay).
• Master: Hugues Talbot . Optimisation for AI, (20h lec + 8h lab) M.Sc in AI, CentraleSupélec.
• Master: Hugues Talbot . Introduction to Machine Learning, MS Management of Technogy, 30h (lec.).
• Master: Maria Vakalopoulou . Introduction to Visual Computing, CentraleSupélec, 25h (lec).
• Master: Maria Vakalopoulou . Introduction to Deep Learning, M.Sc. in Data Sciences and Business Analytics, CentraleSupélec and ESSEC Business School, 24h (lec).
• Master: Maria Vakalopoulou . Introduction to Deep Learning, M.Sc. in Artificial Intelligence, CentraleSupélec, 24h (lec).
• Master: Maria Vakalopoulou . Deep Learning, M.Sc. in Vision and Machine Learning, ENS Paris-Saclay, 25h (lec).
• Master: Maria Vakalopoulou . Deep Learning in Medical Imaging, M.Sc. in Vision and Machine Learning, ENS Paris-Saclay, 25h (lec).

Several students members of OPIS have teaching assistant activities, in the following cursus of the Univ. Paris Saclay campus:

• Bachelor AIDAMS, CentraleSupélec and ESSEC
• ENSTA ParisTech
• CentraleSupélec
• M.Sc. DSBA, Univ. Paris Saclay
• M.Sc. MVA, Univ. Paris Saclay

11.2.2 PhD supervision

• PhD (completed): Gabriele Scrivanti . New algorithms for large scale problems exploiting geometric problem structure and convex relaxation, 2020-2024, supervised by Jean-Christophe Pesquet and E. Bednarczuk (Polish Academy of Sciences, Warsaw).
• PhD (completed): Ségolène Martin . Majorization-Minimization methods for constrained optimization, 2020-2024, supervised by Jean-Christophe Pesquet and I. Ben Ayed (ETS Montréal, Canada).
• PhD (completed): Theodore Aouad . Geometric semi-supervised lachine-learning methods in medical imaging, 2020-2024, supervised by Hugues Talbot .
• PhD (completed): Younes Belkouchi . Graph neural network generative methods in oncology, 2020-2024, supervised by N. Lassau (IGR), Fragkiskos Malliaros , and Hugues Talbot .
• PhD (completed): Mouna Gharbi . Unfolded Majorization-Minimization algorithms, 2020-2023, supervised by Emilie Chouzenoux and L. Duval (IFPEN).
• PhD (completed): Nguyen Vu . Algorithmes d'assemblage pour l'apprentissage en imagerie médicale, 2021-2024, supervised by Emilie Chouzenoux and P. Pinault (ESSILOR).
• PhD (completed): Alexandre Duval . Algorithmes d'apprentissage automatique sur des graphes: explicabilité, évolutivité et applications, 2020-2023, supervised by Fragkiskos Malliaros and Hugues Talbot
• PhD (in progress): Loïc Le Bescond . Precision medicine, Histology and Deep learning, 2021-2024, supervised by F. André (IGR) and Hugues Talbot .
• PhD (in progress): Thomas Guilmeau . Algorithmes stochastiques pour l'optimisation non convexe, 2021-2024, supervised by Emilie Chouzenoux and V. Elvira (Univ. Edinburgh).
• PhD (in progress): Raaja El Hamdani . Robust graph representation learning and applications in misinformation detection, 2021-2024, supervised by Fragkiskos Malliaros and T. Bonald (Télécom-Paris).
• PhD (in progress): Othmane Laousy . Graph-based artificial intelligence methods for medical image diagnosis, 2019-2024, supervised by Maria Vakalopoulou and M.-P. Revel (AP-HP Hospital Cochin).
• PhD (in progress): Clement Cosserat . Algorithmes de majoration-minimisation pour le traitement du signal statistique, 2022-2025, supervised by Emilie Chouzenoux and T. Adali (Univ. Baltimore, USA).
• PhD (in progress): Alix Chazottes . Algorithmes d’optimisation dépliés pour la reconstruction d’images à partir de données TEP dynamique, 2023-2026, supervised by Emilie Chouzenoux and F. Sureau (CEA, Biomaps).
• PhD (in progress): Aymen Sardroui . Histopathological image analysis, 2022-2025, supervised by Mounir Kaaniche and Jean-Christophe Pesquet .
• PhD (in progress): Arsene Amoya. Neural networks-based stereo image retrieval, supervised by Mounir Kaaniche and A. Benazza-Benyahia (SUP'COM-Tunis).
• PhD (in progress): Nabil Mouadden. Deep Learning methods for lung applications, 2023-2026, supervised by Maria Vakalopoulou and G. Chassagnon (Hôpital Cochin).
• PhD (in progress): Jinqwei Zhang. Deep Learning methods on Digital Pathology, 2020-2024, supervised by Maria Vakalopoulou , and D. Samaras (Stony Brook University).
• PhD (in progress): Yassine Abbahaddou . Topics in Geometric Deep Learning, 2022-2025, supervised by Fragkiskos Malliaros , J. Lutzeyer, and M. Vazirgiannis (École Polytechnique).
• PhD (in progress): Vahan Martirosyan . Deep Graph Neural Networks (GNNs) and Applications in Biomedicine, 2023-2026, supervised by Fragkiskos Malliaros , Hugues Talbot , and J. Giraldo (Télécom Paris).
• PhD (in progress): Nicolas Salvy . Génération d'images cérébrales fonctionnelles à grande échelle poru améliorer la cartographie cérébrale, 2023-2026, supervised by Hugues Talbot , and Bertrand Thirion (Inria Saclay, MIND).
• PhD (in progress): Hafsa El Herichi . Extraction du tissage 3D des pièces composites de grandes dimensions à partir d’images tomographiques, 2023-2026, supervised by Hugues Talbot , and Stéphane Roux (Laboratoire de Mécanique de Paris-Saclay, ENS Paris-Saclay).
• PhD (in progress): David Restrepo. Bias Analysis on vision and language models 2024-2027, supervised by Maria Vakalopoulou , Stergios Christodoulidis, Enzo Ferrante and Gilles Fay, MICS, CentraleSupélec.
• PhD (in progress): Adam Ghalem. Graph Neural Networks for Causal Inference for Wireless Network Management, 2024-2027, supervised by Fragkiskos Malliaros .
• PhD (in progress): Vuk Ignjatovic . Generative Models for Motion Correction in Computed Tomography, 2024-2027, supervised by Nora Ouzir , Jean-Christophe Pesquet , and Cyril Riddell (GE Healthcare).
• PhD (in progress): Raoul Sallé de Chou, Machine-learning based Prediction of heart perfusion maps, 2021-2025, supervised by Laurent Najman (UGE), Hugues Talbot , Irene Vignon-Clémentel (Inria SimbotX)
• PhD (in progress): Francesco Songia, Reduced order modelling of hemodynamics for liver surgery procedure, 2025-2028, supervised by Nicolas Golse (APHP), Irene Vignon-Clementel (Inria SimbiotX), Hugues Talbot .

11.2.3 Intern/Engineers/Apprentices supervision

• Maxence Adly , Sep. 2024-Feb. 2025, supervised by Emilie Chouzenoux
• Hugo Pham , Jan-June 2024, supervised by Emilie Chouzenoux and Quentin Vanderbecq
• Gaspard Blaise , Apr.-July 2024, supervised by Clement Cosserat
• Miguel Amorim , Feb.-July 2024, supervised by Antonio Silveti-Falls
• Idriss Benkirane , Sep. 2024-Feb. 2025, supervised by Hugues Talbot
• Benjamin Clene , Nov. 2024-Jan. 2025, supervised by Hugues Talbot
• Mohammad Mehdi Kalla , Sep. 2024-Aug. 2025, supervised by Emilie Chouzenoux ,
• Quentin Mace , May-Oct. 2024, supervised by Nora Ouzir
• Solal Martin , Apr.-Aug. 2024, supervised by Emilie Chouzenoux
• Gabriel Pirlogeanu , Feb.-June 2024, supervised by Jean-Christophe Pesquet
• Soukaina Tichirra , Mar.-Aug. 2024, supervised by Jean-Christophe Pesquet
• Ludovic Trautmann , Dec. 2024-June 2025, supervised by Emilie Chouzenoux
• Clement Lavaud , Mar.-Oct. 2024, supervised by Nguyen Vu
• Caglayan Tuna , May-Oct. 2024, supervised by Nora Ouzir
• Long Zhou, March-June 2024, supervised by Mounir Kaaniche and Jean-Christophe Pesquet .

11.2.4 Juries

The faculty members of the team serve regularly as a jury Member to Final Engineering Internship and the Research Innovation Project for students of CentraleSupélec, and to Research Internship for students of Ms.C. MVA, ENS Paris Saclay.

11.3.1 Participation in Live events

• Alix Chazottes : Group supervision at Rendez-vous des Jeunes Mathématiciennes et Informaticiennes, Inria Saclay, February 2024.
• Alix Chazottes : Intervention at Science en Baskets (in the context of the Paris-Saclay Summit 2024), Inria Saclay, February 2024.
• Alix Chazottes : Animation at Fête de la Science, Inria Saclay, October 2024.

International journals

• 4 articleJ.Julien Ajdenbaum, E.Emilie Chouzenoux, C.Claire Lefort, S.Ségolène Martin and J.-C.Jean-Christophe Pesquet. A Novel Variational Approach for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution.Inverse Problems406April 2024HALDOIback to textback to text
• 5 articleS.Samy Ammari, A.Arnaud Quillent, V.Víctor Elvira, F.François Bidault, G.Gabriel Garcia, D.Dana Hartl, C.Corinne Balleyguier, N.Nathalie Lassau and É.Émilie Chouzenoux. Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.Journal of Imaging Informatics in MedicineOctober 2024, Online ahead of printHALDOIback to text
• 6 articleT.Theodore Aouad, V.Valerie Laurent, P.Paul Levant, A.Agnes Rode, N.Nina Brillat-Savarin, P.Pénélope Gaillot, C.Christine Hoeffel, E.Eric Frampas, M.Maxime Barat, R.Roberta Russo, M.Mathilde Wagner, M.Magaly Zappa, O.Olivier Ernst, A.Anais Delagnes, Q.Quentin Fillias, L.Lama Dawi, C.Céline Savoye-Collet, P.Pauline Copin, P.Paul Calame, E.Edouard Reizine, A.Alain Luciani, M.-F.Marie-France Bellin, H.Hugues Talbot and N.Nathalie Lassau. Detection and characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge.Diagnostic and Interventional Imaging105102024, 395-399HALDOIback to text
• 7 articleR.Rémy Barbe, Y.Younes Belkouchi, Y.Yves Menu, R.Romain Cohen, C.Clemence David, M.Michele Kind, S.Sana Harguem, L.Lama Dawi, J.Joya Hadchiti, F.Fatine Selhane, N.Nicolas Billet, S.Samy Ammari, A.Ambroise Bertin, L.Littisha Lawrance, B.Baptiste Cervantes, A.Antoine Hollebecque, C.Corinne Balleyguier, P.-H.Paul-Henry Cournede, H.Hugues Talbot, N.Nathalie Lassau and T.Thierry Andre. Imaging-guided prognostic score-based approach to assess the benefits of combotherapy versus monotherapy with immune checkpoint inhibitors in metastatic MSI-H colorectal cancer patients.European Journal of Cancer202May 2024, 114020HALDOIback to text
• 8 articleA.Aurélie Beaufrère, N.Nora Ouzir, P. E.Paul Emile Zafar, A.Astrid Laurent-Bellue, M.Miguel Albuquerque, G.Gwladys Lubuela, J.Jules Grégory, C.Catherine Guettier, K.Kévin Mondet, J.-C.Jean-Christophe Pesquet and V.Valérie Paradis. Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning.JHEP Reports Innovation in Hepatology632024, 101008HALDOIback to text
• 9 articleY.Younes Belkouchi, J.-C.Jean-Christophe Pesquet, A.Audrey Repetti and H.Hugues Talbot. Learning truly monotone operators with applications to nonlinear inverse problems: LEARNING TRULY MONOTONE OPERATORS.SIAM Journal on Imaging SciencesMarch 2024. In press. HALback to text
• 10 articleJ.Jérôme Bolte, E.Edouard Pauwels and A. J.Antonio José Silveti-Falls. Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems.SIAM Journal on Optimization341January 2024, 27 p.HALDOIback to text
• 11 articleJ.Jhon Castro-Correa, J.Jhony Giraldo, M.Mohsen Badiey and F. D.Fragkiskos D. Malliaros. Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction.IEEE Transactions on Neural Networks and Learning SystemsApril 2024HALDOIback to textback to text
• 12 articleJ.Jia Chen, C.Congcong Wang, F.Fan Shi, M.Mounir Kaaniche, M.Meng Zhao, Y.Yan Jing and S.Shengyong Chen. DSNet: A Dynamic Squeeze Network for Real-time Weld Seam Image Segmentation.Engineering Applications of Artificial IntelligenceMarch 2024HALback to text
• 13 articleE.Emilie Chouzenoux, M.-C.Marie-Caroline Corbineau, J.-C.Jean-Christophe Pesquet and G.Gabriele Scrivanti. A variational approach for joint image recovery and feature extraction based on spatially varying generalised Gaussian models.Journal of Mathematical Imaging and Vision2024. In press. HALback to text
• 14 articleE.Emilie Chouzenoux and V.Víctor Elvira. Sparse Graphical Linear Dynamical Systems.Journal of Machine Learning Research252232024, 1-53HALback to textback to textback to text
• 15 articleE.Emilie Chouzenoux and J.-B.Jean-Baptiste Fest. Convergence Analysis of Block Majorize-Minimize Subspace Approach.Optimization Letters182024, 1111–1130HALDOIback to text
• 16 articleE.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and F.Fernando Roldán. Solution of Mismatched Monotone+Lipschitz Inclusion Problems.SIAM Journal on Optimization2024. In press. HALDOIback to text
• 17 articleT.Tassnim Dardouri, M.Mounir Kaaniche, A.Amel Benazza-Benyahia and G.Gabriel Dauphin. Multi-Task Convolution Neural Network-Based Lifting Scheme for Image Compression.Pattern Recognition Letters2025HALback to text
• 18 articleL.Lama Dawi, Y.Younes Belkouchi, L.Littisha Lawrance, O.Othilie Gautier, S.Samy Ammari, D.Damien Vasseur, F.Felix Wirth, J.Joya Hadchiti, S.Salome Morer, C.Clemence David, F.François Bidault, C.Corinne Balleyguier, M.Michèle Kind, A.Arnaud Bayle, L.Laila Belcaid, M.Mihaela Aldea, C.Claudio Nicotra, A.Arthur Geraud, M.Madona Sakkal, F.Felix Blanc-Durand, S.Sophie Moog, M. F.Maria Fernanda Mosele, M.Marco Tagliamento, A.Alice Bernard-Tessier, B.Benjamin Verret, C.Cristina Smolenschi, N.Nathalie Auger, A.Anas Gazzah, J.-B.Jean-Baptiste Micol, O.Olivier Caron, A.Antoine Hollebecque, Y.Yohann Loriot, B.Benjamin Besse, L.Ludovic Lacroix, E.Etienne Rouleau, S.Santiago Ponce, F.Fabrice André, J.-C.Jean-Charles Soria, F.Fabrice Barlesi, S.Serge Muller, P.-H.Paul-Henry Cournede, H.Hugues Talbot, A.Antoine Italiano and N.Nathalie Lassau. Liquid Biopsy versus CT: Comparison of Tumor Burden Quantification in 1065 Patients with Metastases.Radiology3132November 2024HALDOIback to text
• 19 articleA.Aissam Djahnine, C.Carole Lazarus, M.Mathieu Lederlin, S.Sébastien Mulé, R.Rafael Wiemker, S.Salim Si-Mohamed, E.Emilien Jupin-Delevaux, O.Olivier Nempont, Y.Youssef Skandarani, M.Mathieu de Craene, S.Segbedji Goubalan, C.Caroline Raynaud, Y.Younes Belkouchi, A. B.Amira Ben Afia, C.Clement Fabre, G.Gilbert Ferretti, C.Constance de Margerie, P.Pierre Berge, R.Renan Liberge, N.Nicolas Elbaz, M.Maxime Blain, P.-Y.Pierre-Yves Brillet, G.Guillaume Chassagnon, F.Farah Cadour, C.Caroline Caramella, M. E.Mostafa El Hajjam, S.Samia Boussouar, J.Joya Hadchiti, X.Xavier Fablet, A.Antoine Khalil, H.Hugues Talbot, A.Alain Luciani, N.Nathalie Lassau and L.Loic Boussel. Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution.Diagnostic and Interventional ImagingMarch 2024HALDOI
• 20 articleA.Aref Einizade, J. H.Jhony H. Giraldo, F. D.Fragkiskos D. Malliaros and S.Sepideh Hajipour Sardouie. Estimation of a causal directed acyclic graph process using non-gaussianity.Digital Signal Processing146March 2024, 104400HALDOIback to text
• 21 articleM.Mouna Gharbi, E.Emilie Chouzenoux and J.-C.Jean-Christophe Pesquet. An Unrolled Half-Quadratic Approach for Sparse Signal Recovery in Spectroscopy.Signal Processing218May 2024HALDOIback to textback to textback to text
• 22 articleT.Thomas Guilmeau, E.Emilie Chouzenoux and V.Víctor Elvira. A Divergence-Based Condition to Ensure Quantile Improvement in Black-Box Global Optimization.IEEE Transactions on Evolutionary ComputationSeptember 2024, 1-1HALDOIback to text
• 23 articleT.Thomas Guilmeau, E.Emilie Chouzenoux and V.Víctor Elvira. On variational inference and maximum likelihood estimation with the $$-exponential family.Foundations of Data Science612024, 85-123HALDOIback to text
• 24 articleT.Thomas Guilmeau and A.Alain Rapaport. Multiplicity of neutrally stable periodic orbits with coexistence in the chemostat subject to periodic removal rate.SIAM Journal on Applied Mathematics841January 2024, 39-59HALDOI
• 25 articleP.Pooja Gupta, A.Anurag Goel, A.Angshul Majumdar, E.Emilie Chouzenoux and G.Giovanni Chierchia. DeConFCluster: Deep Convolutional Transform Learning based Multiview Clustering Fusion Framework.Signal Processing224109597November 2024. In press. HALDOIback to text
• 26 articleS.Simona Juvina, A.-A.Ana-Antonia Neacșu, J.-C.Jean-Christophe Pesquet, B.Burileanu Corneliu, J.Jérôme Rony and I. B.Ismail Ben Ayed. Training Graph Neural Networks Subject to a Tight Lipschitz Constraint.Transactions on Machine Learning Research JournalMay 2024HALback to textback to text
• 27 articleA.Astrid Laurent-Bellue, A.Aymen Sadraoui, L.Laura Claude, J.Julien Calderaro, K.Katia Posseme, E.Eric Vibert, D.Daniel Cherqui, O.Olivier Rosmorduc, M.Maïté Lewin, J.-C.Jean-Christophe Pesquet and C.Catherine Guettier. Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images.American Journal of Pathology1949September 2024, 1684-1700HALDOIback to text
• 28 articleA.Aëlle Le Gall, T.-N.Trieu-Nghi Hoang-Thi, R.Raphaël Porcher, B.Bertrand Dunogué, A.Alice Berezné, L.Loïc Guillevin, V.Véronique Le Guern, P.Pascal Cohen, B.Benjamin Chaigne, J.Jonathan London, M.Matthieu Groh, R.Romain Paule, G.Guillaume Chassagnon, M.Maria Vakalopoulou, A.-T.Anh-Tuan Dinh-Xuan, M. P.Marie Pierre Revel, L.Luc Mouthon and A.Alexis Régent. Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.Rheumatology631January 2024, 103-110HALDOIback to text
• 29 articleM.Martinez Leonard, A.Andrieu François, S.Schmidt Frédéric, H.Hugues Talbot and B.Bentley Mark. Robust automatic crater detection at all latitudes on Mars with Deep-learning.Planetary and Space Science2025. In press. HALback to text
• 30 articleA.-A.Ana-Antonia Neacșu, J.-C.Jean-Christophe Pesquet, V.Vlad Vasilescu and C.Corneliu Burileanu. ABBA Neural Networks: Coping with Positivity, Expressivity, and Robustness.SIAM Journal on Mathematics of Data Science632024. In press. HALDOIback to text
• 31 articleJ.Joeri Nicolaes, E.Evi Tselenti, T.Theodore Aouad, C.Clementina López-Medina, A.Antoine Feydy, H.Hugues Talbot, B.Bengt Hoepken, N.Natasha de Peyrecave and M.Maxime Dougados. Performance analysis of a deep-learning algorithm to detect the presence of inflammation in MRI of sacroiliac joints in patients with axial spondyloarthritis.Annals of the Rheumatic DiseasesJanuary 2025HALDOIback to text
• 32 articleN.Nora Ouzir, F.Frédéric Pascal and J.-C.Jean-Christophe Pesquet. Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions.IEEE Transactions on Signal ProcessingSeptember 2024HALback to textback to text
• 33 articleG.George Panagopoulos, N.Nikolaos Tziortziotis, M.Michalis Vazirgiannis, J.Jun Pang and F. D.Fragkiskos D. Malliaros. Learning graph representations for influence maximization.Social Network Analysis and Mining141October 2024, 203HALDOIback to text
• 34 articleJ.-C.Jean-Christophe Pesquet, C.Corneliu Burileanu and A.-A.Ana-Antonia Neacșu. EMG-Based Automatic Gesture Recognition Using Lipschitz-Regularized Neural Networks.ACM Transactions on Intelligent Systems and TechnologyFebruary 2024HALback to text
• 35 articleC.Carlos Santos, M.Mathilde Larchevêque, S.Solal O'Sullivan, M.Martin van Waerebeke, R. R.Robert R Thomson, A.Audrey Repetti and J.-C.Jean-Christophe Pesquet. A Primal-Dual Data-Driven Method for Computational Optical Imaging with a Photonic Lantern.PNAS Nexus34April 2024, pgae164HALDOIback to text
• 36 articleQ.Quentin Vanderbecq, M.Maxence Gelard, J.-C.Jean-Christophe Pesquet, M.Mathilde Wagner, L.Lionel Arrive, M.Marc Zins and E.Emilie Chouzenoux. Deep Learning for Automatic Bowel-Obstruction Identification on Abdominal CT.European Radiology34January 2024, 5842-5853HALback to textback to text
• 37 articleG.Guillaume Vidon, G.Gabriele Scrivanti, E.Etienne Soret, N.Nao Harada, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet, J.Jean‐françois Guillemoles and S.Stefania Cacovich. Rapid and Noise‐Resilient Mapping of Photogenerated Carrier Lifetime in Halide Perovskite Thin Films.Advanced Functional Materials3437April 2024HALDOIback to text

Invited conferences

• 38 inproceedingsE.Emilie Chouzenoux and V.Víctor Elvira. Graphical inference in non-Markovian linear-Gaussian state-space models.ICASSP 2024 - IEEE International Conference on Acoustics, Speech and Signal ProcessingSeoul, South KoreaApril 2024HALDOIback to textback to text

International peer-reviewed conferences

• 39 inproceedingsT.Theodore Aouad and H.Hugues Talbot. A foundation for exact binarized morphological neural networks.ICCV 2023 - International Conference on Computer Vision, workshop on Low Bit Quantized Neural NetworksParis, FranceJanuary 2024HALback to text
• 40 inproceedingsA.Alix Chazottes, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and F.Florent Sureau. Deep Equilibrium for Hyperparameter Estimation in Dynamic PET Reconstruction.MIC 2024 - IEEE Medical Imaging ConferenceTampa (Florida), United StatesOctober 2024HALback to text
• 41 inproceedingsA.Aref Einizade, F. D.Fragkiskos D. Malliaros and J. H.Jhony H. Giraldo. Continuous Product Graph Neural Networks.Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024NeurIPS 2024 - 38th Conference on Neural Information Processing SystemsVancouver (BC), CanadaOctober 2024HALback to textback to text
• 42 inproceedingsR.Rajaa El Hamdani, T.Thomas Bonald, F. D.Fragkiskos D. Malliaros, N.Nils Holzenberger and F. M.Fabian M. Suchanek. The Factuality of Large Language Models in the Legal Domain.CIKM 2024 - 33rd ACM International Conference on Information and Knowledge ManagementBoise Idaho, United StatesOctober 2024HALback to text
• 43 inproceedingsM.Mouna Gharbi, S.Silvia Villa, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and L.Laurent Duval. Unrolled deep networks for sparse signal restoration in analytical chemistry.Proceedings of the 34th IEEE International Workshop on Machine Learning for Signal Processing (IEEE MLSP 2024)MLSP 2024 - 34th IEEE International Workshop on Machine Learning for Signal ProcessingLondres, United Kingdom2024HALback to textback to text
• 44 inproceedingsY.Yi Gu, Y.Yoshito Otake, K.Keisuke Uemura, M.Masaki Takao, M.Mazen Soufi, S.Seiji Okada, N.Nobuhiko Sugano, H.Hugues Talbot and Y.Yoshinobu Sato. 3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimation.Medical Image Computing and Computer Assisted Intervention – MICCAI 2024MICCAI 202415007Lecture Notes in Computer ScienceMarrakech, MoroccoSpringer Nature SwitzerlandOctober 2024, 3-13HALDOIback to text
• 45 inproceedingsY.Yi Gu, Y.Yoshito Otake, K.Keisuke Uemura, M.Masaki Takao, M.Mazen Soufi, S.Seiji Okada, N.Nobuhiko Sugano, H.Hugues Talbot and Y.Yoshinobu Sato. Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Image.Lecture Notes in Computer ScienceInternational Workshop on Simulation and Synthesis in Medical Imaging15187Marrakech, MoroccoAugust 2024, 134–145HALback to text
• 46 inproceedingsT.Thomas Guilmeau, N.Nicola Branchini, E.Emilie Chouzenoux and V.Víctor Elvira. Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization.Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS)AISTATS 2024 - 27th International Conference on Artificial Intelligence and StatisticsValencia, SpainMay 2024HALback to text
• 47 inproceedingsM.Marcin Jankowski, E.Emila Lewandowska, H.Hugues Talbot, D.Daniel Wesierski and A.Anna Jezierska. Learning sperm cells part segmentation with class-specific data augmentation.HSI 2024 - 16th International Conference on Human System InteractionParis, FranceIEEEJuly 2024, 1-6HALDOIback to text
• 48 inproceedingsS.Saarthak Kapse, P.Pushpak Pati, S.Srijan Das, J.Jingwei Zhang, C.Chao Chen, M.Maria Vakalopoulou, J.Joel Saltz, D.Dimitris Samaras, R. R.Rajarsi R. Gupta and P.Prateek Prasanna. SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology.CVPR 2024 - Computer Vision and Pattern RecognitionSeattle, United StatesJune 2024HALback to text
• 49 inproceedingsY.Yingping Li, E.Emilie Chouzenoux, S.Shuiping Gou, P.Peng Zeng, S.Samy Ammari and N.Nathalie Lassau. ComBat-MLE: A novel perspective on developing and solving ComBat harmonization method.IEEE Medai 2024 - The second IEEE international conference on medical artificial intelligenceChongqing, ChinaNovember 2024HAL
• 50 inproceedingsD.Daniele Malitesta, E.Emanuele Rossi, C.Claudio Pomo, T.Tommaso Di Noia and F. D.Fragkiskos D Malliaros. Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementCIKM 2024 - The 33rd ACM International Conference on Information and Knowledge ManagementCIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge ManagementBoise, ID, United StatesACMOctober 2024, 3943-3948HALDOIback to textback to text
• 51 inproceedingsS.Ségolène Martin, J.Julien Ajdenbaum, E.Emilie Chouzenoux, L.Laetitia Magnol, V.Véronique Blanquet, J.-C.Jean-Christophe Pesquet and C.Claire Lefort. Estimating the point-spread-function using 1 µm diameter microspheres for image restoration in biomedical multiphoton microscopy.SPIE Photonics Europe 2024Strasboug, FranceSPIEJuly 2024, 22HALDOIback to textback to text
• 52 inproceedingsS.Ségolène Martin, Y.Yunshi Huang, F.Fereshteh Shakeri, J.-C.Jean-Christophe Pesquet and I. B.Ismail Ben Ayed. Transductive Zero-Shot and Few-Shot CLIP.CVPR 2024 - IEEE Conference on Computer Vision and Pattern RecognitionSeattle, Washington, United StatesJune 2024HALback to textback to text
• 53 inproceedingsJ.Julie Mordacq, L.Leo Milecki, M.Maria Vakalopoulou, S.Steve Oudot and V.Vicky Kalogeiton. ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities.Proceedings of Machine Learning ResearchMIDL 2024 - Medical Imaging with Deep LearningParis, FranceJuly 2024HAL
• 54 inproceedingsT.Théo Moutakanni, M.Maxime Oquab, M.Marc Szafraniec, M.Maria Vakalopoulou and P.Piotr Bojanowski. You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning.NeurIPS 2024 - The Thirty-Eighth Annual Conference on Neural Information Processing SystemsVancouver, CanadaDecember 2024HALback to text
• 55 inproceedingsG.George Panagopoulos, D.Daniele Malitesta, F. D.Fragkiskos D Malliaros and J.Jun Pang. Uplift Modeling Under Limited Supervision.ECML PKDD 2024 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in DatabasesVilnius, LithuaniaJune 2024HALback to textback to text
• 56 inproceedingsA.Aymen Sadraoui, A.Astrid Laurent-Bellue, M.Mounir Kaaniche, A.Amel Benazza-Benyahia, C.Catherine Guettier and J.-C.Jean-Christophe Pesquet. Unrolled projected gradient algorithm for stain separation in digital histopathological images.ICIP 2024 - IEEE International Conference on Image ProcessingAbu Dhabi, United Arab EmiratesJune 2024HALback to textback to text
• 57 inproceedingsA.Aymen Sadraoui, S.Ségolène Martin, E.Eliott Barbot, A.Astrid Laurent-Bellue, J.-C.Jean-Christophe Pesquet, C.Catherine Guettier and I. B.Ismail Ben Ayed. A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer.ISBI 2024 - 21st IEEE International Symposium on Biomedical ImagingProceedings of the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)Athènes, GreeceMay 2024HALDOIback to textback to text
• 58 inproceedingsR.Raoul Sallé de Chou, M.Matthew Sinclair, S.Sabrina Lynch, N.Nan Xiao, L.Laurent Najman, I. E.Irene E Vignon-Clementel and H.Hugues Talbot. Finite Volume Informed Graph Neural Network for Myocardial Perfusion Simulation.MIDL 2024 - Medical Imaging with Deep Learning 2024Paris, FranceJuly 2024HALback to textback to text
• 59 inproceedingsR.Raoul Sallé de Chou, M. A.Mohamed Ali Srir, L.Laurent Najman, N.Nicolas Passat, H.Hugues Talbot and I.Irene Vignon-Clementel. Convex optimization for binary tree-based transport networks.Lecture Notes in Computer ScienceInternational Conference on Discrete Geometry and Mathematical Morphology (DGMM)14605Florence, Italy2024, 217–228HALDOIback to text
• 60 inproceedings V.Vlad Vasilescu, A.Ana Neacsu and J.-C.Jean-Christophe Pesquet. Are almost non-negative neural networks universal approximators? MLSP 2024 - IEEE International Workshop on Machine Learning for Signal Processing Search form Search London, United Kingdom September 2024 HAL
• 61 inproceedingsG.Guillaume Vidon, G.Gabriele Scrivanti, E.Etienne Soret, N.Nao Harada, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet, J.-F.Jean-François Guillemoles and S.Stefania Cacovich. Rapid and Noise-Resilient Mapping of Photogenerated Carrier Lifetime in Halide Perovskite Thin Films.MRS Fall MeetingBoston (MA), United StatesDecember 2024HAL
• 62 inproceedingsM.Mathieu Vu, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet, S.Stéphane Boutinon, M.Marius Peloux and P.Philippe Pinault. Deep learning method for accessible eccentric photorefraction.ISBI 2024 - 21st IEEE International Symposium on Biomedical ImagingProceedings of the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)Athènes, GreeceMay 2024HALback to textback to text

Conferences without proceedings

• 63 inproceedingsJ.Julie Mordacq, L.Leo Milecki, M.Maria Vakalopoulou, S.Steve Oudot and V.Vicky Kalogeiton. Multimodal Learning for Detecting Stress under Missing Modalities.WiCV 2024 - Women in Computer Vision workshop In conjunction with CVPRSeattle, United StatesJune 2024HAL

Scientific book chapters

• 64 inbookA.Alessandro Benfenati, E.Emilie Chouzenoux, G.Giorgia Franchini, S.Salla Latva-Äijö, D.Dominik Narnhofer, J.-C.Jean-Christophe Pesquet, S.Sebastian J. Scott and M.Mahsa Yousefi. Majoration-Minimization for Sparse SVMs.61Advanced Techniques in Optimization for Machine Learning and ImagingSpringer INdAM Series - INdAM Workshop: Advanced Techniques in Optimization for Machine learning and ImagingSpringer Nature SingaporeOctober 2024, 31-54HALDOIback to text

Doctoral dissertations and habilitation theses

• 65 thesisF. D.Fragkiskos D. Malliaros. A Machine Learning Tour in Network Science.Universite Paris-SaclayApril 2024HAL

Reports & preprints

• 66 miscS.Sayantika Chatterjee, A.Angshul Majumdar and E.Emilie Chouzenoux. Antibiotic Bacteria Interaction: Dataset and Benchmarking.January 2024HALback to textback to textback to text
• 67 miscE.Emilie Chouzenoux, C.Cecile Della Valle and J.-C.Jean-Christophe Pesquet. Stability Bounds for the Unfolded Forward-Backward Algorithm.December 2024HALback to textback to text
• 68 miscC.Clément Cosserat, B.Ben Gabrielson, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet, J.-C.Jean-Christophe Pesquet and T.Tülay Adali. An Effective Iterative Solution for Independent Vector Analysis with Convergence Guarantees.November 2024HALback to text
• 69 miscB.Benjamin Cox, E.Emilie Chouzenoux and V.Víctor Elvira. GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models.November 2024HALback to text
• 70 miscJ.-L.Jean-Luc Dupire, Y.Younes Belkouchi, L.Leo Stepien, N.Nicolas Billet, L.Lama Dawi, F.Felix Wirth, N.Nathalie Lassau and H.Hugues Talbot. 3D lung nodule segmentation from 2D annotations using morphological operations.January 2024HAL
• 71 miscA.Alexandre Duval, S. V.Simon V Mathis, C. K.Chaitanya K Joshi, V.Victor Schmidt, S.Santiago Miret, F. D.Fragkiskos D. Malliaros, T.Taco Cohen, P.Pietro Liò, Y.Yoshua Bengio and M.Michael Bronstein. A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.January 2024HAL
• 72 miscJ.-B.Jean-Baptiste Fest, A.Audrey Repetti and E.Emilie Chouzenoux. A stochastic use of the Kurdyka-Lojasiewicz property: Investigation of optimization algorithms behaviours in a non-convex differentiable framework.November 2024HALback to text
• 73 miscP.Pedro Gajardo, T.Thomas Guilmeau and C.Cristopher Hermosilla. Sensitivity Analysis of the Set of Sustainable Thresholds.May 2024HAL
• 74 miscT.Thomas Guilmeau, E.Emilie Chouzenoux and V.Víctor Elvira. A divergence-based condition to ensure quantile improvement in black-box global optimization.June 2024HAL
• 75 reportT.Thomas Guilmeau, E.Emilie Chouzenoux and V.Víctor Elvira. Regularized Rényi divergence minimization through Bregman proximal gradient algorithms.Inria Saclay - Île de France2024HAL
• 76 miscL.Loïc Le Bescond, M.Marvin Lerousseau, F.Fabrice Andre and H.Hugues Talbot. SparseXMIL: Leveraging sparse convolutions for context-aware and memory-efficient classification of whole slide images in digital pathology.April 2024HALback to text
• 77 misc D.Daniele Malitesta, G.Giacomo Medda, E.Erasmo Purificato, L.Ludovico Boratto, F. D.Fragkiskos D. Malliaros, M.Mirko Marras and E. W.Ernesto William de Luca. How Fair is Your Diffusion Recommender Model? September 2024 HAL
• 78 miscJ.Joeri Nicolaes, E.Evi Tselenti, T.Theodore Aouad, C.Clementina López-Medina, A.Antoine Feydy, H.Hugues Talbot, B.Bengt Hoepken, N.Natasha de Peyrecave and M.Maxime Dougados. Performance Analysis of a Deep Learning Algorithm to Detect MRI Positive Sacroiliac Joints in Patients with Axial Spondyloarthritis According to the Assessment of SpondyloArthritis international Society 2009 Definition.January 2024HAL
• 79 miscN.Nora Ouzir, V.Vassili Pustovalov, D.-H.Duong-Hung Pham, D.Denis Kouamé and J.-C.Jean-Christophe Pesquet. A Proximal Algorithm for Joint Blood Flow Computation and Tissue Motion Compensation in Doppler Ultrafast Ultrasound Imaging.2025HALback to text
• 80 miscL.Leo Stepien, Y.Younes Belkouchi, J.-L.Jean-Luc Dupire, N.Nicolas Billet, L.Lama Dawi, F.Felix Wirth, N.Nathalie Lassau and H.Hugues Talbot. Supervoxel based 3D diseased lung segmentation.January 2024HAL
• 81 miscM.Mathieu Vu, E.Emilie Chouzenoux, I. B.Ismail Ben Ayed and J.-C.Jean-Christophe Pesquet. Aggregated f -average Neural Network applied to Few-Shot Class Incremental Learning.January 2025HALback to text

Other scientific publications

• 82 inproceedingsL.L. Le Bescond, V.V. Suciu, S.S. Christodoulidis, M.M. Vakalopoulou, H.Hugues Talbot, R.R. Soufan, C.C. Balleyguier, S.S. Delaloge and F.F. André. 171P A deep learning model for breast cancer diagnosis from fine-needle aspiration cytology whole slide images.ESMO Open9Berlin, GermanyMay 2024, 103193HALDOI

Patents

• 83 patentM.Manel Ben Youssef, D.Diop Maissa, S.Sylvain Lempereur, M.Menetrey Emile and H.Hugues Talbot. Method for determining whether an image of a person's face is suitable for use as an ID photograph.US 2024/0338977FranceOctober 2024HALback to text