2023Activity 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 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).

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 54, 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.

On this topic, an open-source CCO implementation was published on IPOL (Image Processing OnLine) 22. This has the potential to disseminate this useful method for vascular tree generation to a wider audience.

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 , Paul Zheng (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 the problem of joint trend removal and blind deconvolution of sparse peaklike signals. The trend accounts for mere offsets to slowly-varying amplitude shifts (seasonality, calibration distortion, sensor decline), making its automated removal challenging. We proposed the method PENDANTSS 30 that handles smooth trend removal by exploiting their low-pass property and simplifies the problem into a blind deconvolution problem. The proposed approach implements a convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. Simulation results confirm that PENDANTSS outperforms comparable methods on typical sparse analytical signals. Collaboration with Dr. L. Duval, Research Engineer at IFP Energies Nouvelles, France is on-going in this applicative context.

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 is 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 6, and to visualize bacteria and viral structures 13.

4.3.3 Reconstruction approaches in interventional CT imaging

Participants:Emilie Chouzenoux , Fernando Marcelo Roldan Contreras , Jean-Christophe Pesquet , Marion Savanier (Collaboration: C. Riddel, Y. Trousset, GE Healthcare)

The objective of this collaboration with researchers from GE Healthcare, is to develop high quality reconstruction methodologies, for computed tomography (CT) for interventional surgery. Discretizing and implementing tomographic forward and backward operations is a crucial step in the design of model-based iterative reconstruction algorithms in interventional CT, that we have investigated in the CIFRE PhD thesis of Marion Savanier. The mathematical constraint of symmetry on the projector/backprojector pair prevents linear interpolation, which is a standard method in analytical reconstruction, to be used. Consequently, it often happens that these operators are approximated numerically, so that the adjoint property is no longer fulfilled. In 8, we investigate fixed point algorithms stability properties when such an adjoint mismatch arises. In 27, we focus on reconstructing regions of interest from a limited number of CT measurements. We proposed to handle few-view truncated data thanks to a robust non-convex data fidelity term combined with a sparsity inducing regularization function. We then apply the deep unfolding paradigm to unroll a proximal algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner, with a reduced inference time.

4.3.4 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.

4.4.1 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.

4.4.2 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 36. 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.

4.4.3 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 )

Through the Associate International Inria Team COMPASS, led by Emilie Chouzenoux , we 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 19, 21.

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.

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

Participants:Emilie Chouzenoux (Collaboration: V. Elvira, Univ. Edinburgh, UK ; J. Camps-Valls, Univ. Valencia, Spain)

Studying the complex inter-dependencies in climate processes is a critical challenge. We use climate models and analytical data to gain insight through observational causal discovery 31. We revisit Granger causality under a graphical perspective of state-space models. We investigate expectation-maximisation algorithms 3768 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. We evaluate our methods on climate-related problems, such as linking ENSO and the North Atlantic Oscillation.

4.5.1 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.

4.5.2 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.

4.5.3 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 articles [link]68.
• The codes provided are distributed under CeCILL-B V1 license.

7.1.8 Matrix factorization for drug discovery

Web site: Matrix factorization for drug discovery

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: The code is produced by several students from IIIT Delhi, in the context of the Inria International Associate Team COMPASS.
• Duration of the Development: This software has been developped during the years 2022 and 2023.
• This is a set of Matlab codes, to reproduce the experimental results from the articles [link], 21, 19.
• 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 PMCNet

Web site: PMCNet

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code was produced by the OPIS PhD student Yunshi Huang .
• Duration of the Development: This software was developed during the PhD thesis of Y. Yuang, mostly during the year 2023.
• This is a Python/Pytorch code allowing to reproduce the results of the paper 20.
• The codes provided are distributed under a GNU General Public License v3.0.

7.1.11 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 16.
• The codes provided are distributed under a GNU General Public License v3.0.

7.1.12 FAENet

Web site: FAENet

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code has been produced by OPIS PhD student Alexandre Duval .
• Duration of the Development: This software is developed during the PhD thesis of A. Duval in 2023.
• This is a Python code allowing to reproduce the results of the paper 39.
• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.13 SJLR

Web site: SJLR

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code has been produced by J. Giraldo during his visit in OPIS in 2022.
• Duration of the Development: This software is developed in the context of a collaboration with J. Giraldo (Télécom Paris).
• This is a Python code allowing to reproduce the results of the paper 42.
• The codes provided are distributed under the licence GNU General Public License v3.0.

7.1.14 GLIE

Web site: GLIE

• Software Family: vehicle
• Audience: community
• Evolution and maintenance: basic
• Context/Role of OPIS: This code has been produced by J. Giraldo during his visit in OPIS in 2022.
• Duration of the Development: This software is developed in the context of a collaboration with J. Giraldo (Télécom Paris).
• This is a Python code allowing to reproduce the results of the paper 42.
• The codes provided are distributed under the licence GNU General Public License v3.0.

10.1.1 Inria associate team not involved in an IIL or an international program

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 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, IABM congress, March 2023, Paris.
• Emilie Chouzenoux : Contributory talk, workshop ILLS - DATAIA, May 2023, Saclay.
• Emilie Chouzenoux : Seminar AgroParisTech, Oct. 2023, Saclay.
• Emilie Chouzenoux : Seminar LS2N, Dec. 2023, Nantes.
• Thomas Guilmeau : Invited talk, Jun. 2023, MOP Research Seminar, Mathematical Optimization for Data Science Group, Saarland University, Germany.
• Fragkiskos Malliaros : Invited talk, HEALth and THerapeutic Innovation (HEALTHI) consortium, Univ. Paris-Saclay, Feb. 2023.
• Fragkiskos Malliaros : Invited talk, Board of European Students of Technology (BEST), Paris, April 2023.
• Jean-Christophe Pesquet : Keynote speaker at 9th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2023), May 2023, Sardinia.
• Jean-Christophe Pesquet : Lecturer at Plenoptima Training School, June 2023, Rennes.
• Fernando Marcelo Roldan Contreras : Invited talk, workshop Deep learning, image analysis, inverse problems, and optimization (DIPOpt), Nov. 2023, Lyon.
• Tony Silveti-Falls : Invited talk, workshop Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems (ICIAM), Aug. 2023, Tokyo, Japan.
• Tony Silveti-Falls : Seminar ENS Lyon, Apr. 2023, Lyon.
• Tony Silveti-Falls : Seminar at CDS, ENS ULM, June 2023, Paris.
• Tony Silveti-Falls : Invited talk,SIAM Conference on Optimization (SIOP), June 2023, Philadelphia, USA.
• Hugues Talbot : Invited talk, Kremlin-Bicetre hospital, Jan. 2023, Créteil.
• Hugues Talbot : Invited talk, International Symposium on Continuum Models and Discrete Systems, Jun. 2023, Paris.
• Hugues Talbot : Invited talk, 18th colloquium of the Société Française des Microscopies, July 2023, Rouen.
• Hugues Talbot : Invited talk, Laboratoire de Mécanique Paris-Saclay ENS Paris-Saclay, Dec. 2022, Saclay.
• Maria Vakalopoulou : Keynote talk, European Society for Therapeutic Radiology and Oncology (ESTRO) congress, May 2023, Vienna, Austria.
• Maria Vakalopoulou : Invited talk,2nd Transatlantic Exchange Program, Deep Learning Methods for Medical Imaging in Precision Oncology, May 2023, Paris.
• Maria Vakalopoulou : Invited talk,Workshop ILLS - Institut DATAIA, May 2023, Saclay.
• Maria Vakalopoulou : Invited talk,Hellenic Institute of Advanced Studies (HIAS) workshop on AI, July 2023, Athens, Greece.
• Maria Vakalopoulou : Oral presentation at the Association pour la Recherche sur les Tumeurs de Rein (A.R.Tu.R.) workshop, Oct. 2023, Paris.
• Maria Vakalopoulou : Invited talk,International Symposium on Innovative Radation Therapies (INANOTHERAD), Oct. 2023, Saclay.

11.1.5 Leadership within the scientific community

• Jean-Christophe Pesquet : Senior honorary member of the Institut Universitaire de France. Fellow of IEEE and EURASIP.
• 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 : Senior member of the IEEE.
• Nora Ouzir : Elected member of the EURASIP Biomedical Image and Signals Analytics TAC.

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 Inria Saclay.
• 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).
• Nora Ouzir is an elected member of CCUPS, Consultative Commission of Paris-Saclay University.
• Maria Vakalopoulou served as a panel member for ANR in 2023.
• 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.
• Maria Vakalopoulou is the group leader of the $\beta$iomathematics teams of the MICS CentraleSupélec.
• Maria Vakalopoulou is now affiliated with Archimedes Unit in Greece.
• Emilie Chouzenoux is member of the Executive Board of the H2020 ITNMarie Sklodowska-Curie.

11.1.8 Teaching administration

• Emilie Chouzenoux : attached professor in AI in CentraleSupélec, since sep. 2022.
• Fragkiskos Malliaros : co-director of the DSBA M.Sc.
• Fragkiskos Malliaros : head for the Data and Information Science (SDI) specialization at CentraleSupélec.
• Nora Ouzir : Co-coordinator of the Bachelor in Artificial Intelligence, Data and Management Sciences, AIDAMS, with ESSEC Business School and 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 : local head of the MVA M.Sc. at CentraleSupélec.

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: 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, 27h (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 course, CentraleSupélec, FR
• Master: Jean-Christophe Pesquet . Advanced course on Optimization, 10h (lec.), M.Sc. in Signal Processing and Automatic Control, Univ. Paris- Saclay, FR
• Master: Jean-Christophe Pesquet . Introduction to Optimization, 6h (lec.), M.Sc. MVA, ENS Paris-Saclay, FR
• Master: Jean-Christophe Pesquet . Convex Optimization Algorithms, 15h (lec.), M.Sc. in Optimization, Univ. Paris-Saclay, FR
• Master: Hugues Talbot . High-performance computing, 12h (lec.), 2nd year course, CentraleSupélec, FR
• Master: Hugues Talbot . Parallel computing, 20h (lec.), 2nd year course, CentraleSupélec, FR
• Master: Hugues Talbot . Introduction à la morphologie mathématique, 12h (lec.), 3rd year course, CentraleSupélec, FR
• Master: Hugues Talbot . Modern Mathematical Morphology, 28h (lec.), 3rd year course, CentraleSupélec, M.Sc. MVA, ENS Paris-Saclay, FR
• Master: Hugues Talbot . Modern Mathematical Morphology, 30h (lec.), M.Sc in AI, CentraleSupélec, FR
• Master: Hugues Talbot . Introduction to Machine Learning, 30h (lec.), MS Management of Technology, CentraleSupélec, FR
• Master: Tony Silveti-Falls . Convergence, Integration, and Probability, 16.5h (lec.) + 15h (lab), 1st year course, CentraleSupélec, FR
• Master: Tony Silveti-Falls . Optimization for Computer Vision, 24h (lec. + lab), 3rd year course, CentraleSupélec, FR
• Master: Tony Silveti-Falls . Partial Differential Equations, 13.5h (lab), 1st year course, CentraleSupélec, FR
• Master: Tony Silveti-Falls . Optimization, 15h (lab), 2nd year course, CentraleSupélec, FR
• Master: Tony Silveti-Falls . Parcours Recherche, 48h (project), 1st and 2nd year course, CentraleSupélec, FR
• Bachelor: Tony Silveti-Falls . Analysis, 18h (lab), First year Bachelors of Global Engineering students, Univ. Paris Saclay, EN.
• Master: Maria Vakalopoulou . Introduction to Visual Computing, 25h (lec.), CentraleSupélec, FR.
• Master: Maria Vakalopoulou . Introduction to Deep Learning, M.Sc. in Data Sciences and Business Analytics, 24h (lec.), CentraleSupélec and ESSEC Business School, FR.
• Master: Maria Vakalopoulou . Introduction to Deep Learning, M.Sc. in Artificial Intelligence, 24h (lec.), CentraleSupélec, FR.
• Master: Maria Vakalopoulou . Deep Learning, M.Sc. in Vision and Machine Learning, 25h (lec.), ENS Paris-Saclay, FR.
• Master: Maria Vakalopoulou . Deep Learning in Medical Imaging, M.Sc. in Vision and Machine Learning, 25h (lec.), ENS Paris-Saclay, FR.

Several students members of OPIS (Aymen Sardroui , Clement Cosserat , Jean-Baptiste Fest , Thomas Guilmeau , Ségolène Martin , Younes Belkouchi , Theodore Aouad , Alexandre Duval , Loïc Le Bescond ) have teaching assistant activities, in the following cursus of the Univ. Paris Saclay campus:

• ENSTA ParisTech
• IUT Paris Saclay
• CentraleSupélec
• M.Sc. DSBA, Univ. Paris Saclay
• M.Sc. MVA, Univ. Paris Saclay

11.2.2 PhD supervision

• PhD (completed): Jean-Baptiste Fest .Stochastic Majorization-Minimization algorithms, 2020-2023, supervised by Emilie Chouzenoux .
• PhD (completed): Ana Neacsu . Méthodes d'apprentissage profond inspirées d'algorithmes de traitement du signal, 2019-2023, supervised by Jean-Christophe Pesquet and C. Burileanu (Politehnica Bucarest).
• PhD (completed): Sagar Verma . Modélisation, contrôle et supervision de moteurs électriques par réseaux de neurones profonds, 2019-2023, supervised by M. Castella and Jean-Christophe Pesquet .
• PhD (completed): Tassnim Dardouri . Towards a New Generation of Lifting-Based Image Coders Using Neural Networks, 2019-2023, supervised by Mounir Kaaniche and G. Dauphin (Univ. Paris Nord), in collaboration with Jean-Christophe Pesquet .
• PhD (completed): Kavya Gupta . Neural network solutions for safety of complex systems, 2019-2023, supervised by Jean-Christophe Pesquet , Fragkiskos Malliaros , and F. Kaakai (Thales Group).
• PhD (completed): Maria Papadomanolaki, Change Detection from Multitemporal High Resolution Data with Deep Learning, 2017-2022, supervised by Maria Vakalopoulou and K. Karantzalos (National Technical University of Athens).
• PhD (completed): Leo Milescki, Domain adaptation and self supervised methods for kidney transplantation, 2019-2023, supervised by Maria Vakalopoulou and M.O. Timsit (Hôpital Européen Georges-Pompidou).
• PhD (completed): Marvin Lerousseau. Apprentissage statistique en imagerie médicale et en génomique pour prédire l'efficacité des thérapies anti-tumorales, 2018-2022, supervised by N. Paragios (Therapanacea), E. Deutsch (IGR) and Hugues Talbot .
• PhD (completed): Georgios Panagopoulos, Influence maximization in social networks, 2018-2022, supervised by Fragkiskos Malliaros and M. Vazirgiannis (Ècole Polytechnique).
• PhD (completed): Surabhi Jagtap, Graph-based learning from multi-omics data, 2019-2022, supervised by Fragkiskos Malliaros , Jean-Christophe Pesquet , and L. Duval (IFP Energies Nouvelles).
• PhD (in progress): Gabriele Scrivanti . New algorithms for large scale problems exploiting geometric problem structure and convex relaxation, 2020-2023, supervised by Jean-Christophe Pesquet and E. Bednarczuk (Polish Academy of Sciences, Warsaw).
• PhD (in progress): Ségolène Martin . Majorization-Minimization methods for constrained optimization, 2020-2023, supervised by Jean-Christophe Pesquet and I. Ben Ayed (ETS Montréal, Canada).
• PhD (completed): Marie-Charlotte Poilpre: Méthode de comparaison faciale morphologique, adaptée aux expertise judiciaires, basée sur la modélisation 3D, 2017-2023, supervised by Hugues Talbot and V. Nozick (Univ. Paris-Est).
• PhD (in progress): Theodore Aouad . Geometric semi-supervised lachine-learning methods in medical imaging, 2020-2023, supervised by Hugues Talbot .
• PhD (in progress): Younes Belkouchi . Graph neural network generative methods in oncology, 2020-2023, supervised by N. Lassau (IGR), Fragkiskos Malliaros , 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): Mouna Gharbi . Unfolded Majorization-Minimization algorithms, 2020-2023, supervised by Emilie Chouzenoux and L. Duval (IFPEN).
• PhD (in progress): Nguyen Vu . Algorithmes d'assemblage pour l'apprentissage en imagerie médicale, 2021-2024, supervised by Emilie Chouzenoux and P. Pinault (ESSILOR).
• PhD (in progress): 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): 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).

11.2.3 Intern supervision

• Alix Chazottes , May-Oct. 2023, supervised by Emilie Chouzenoux and Quentin Vanderbecq (Hôpital St Joseph)
• Vuk Ignjatovic , Mar.-Sep. 2023, supervised by Nora Ouzir
• Théau Blanchard , May-Oct. 2023, supervised by Nora Ouzir
• Nicolas Dunou , May-Sep. 2023, supervised by Fragkiskos Malliaros
• Jean-Luc Dupire , June-Aug. 2023, supervised by Hugues Talbot
• Vahan Martirosyan , May-Sep. 2023, supervised by Fragkiskos Malliaros
• Corentin Pla , June-Oct. 2023, supervised by Jean-Christophe Pesquet
• Antoine Siraudin , June-Nov. 2023, supervised by Fragkiskos Malliaros
• Léo Stepien , June-Nov. 2023, supervised by Léo Stepien
• Roxane Housny , Apr.-June 2023, supervised by Aymen Sardroui and Jean-Christophe Pesquet
• Eliott Barbot , May-Oct. 2023, supervised by Ségolène Martin , Aymen Sardroui , and Jean-Christophe Pesquet
• Mahuton Hugues Midingoyi , Apr.-Sep. 2023, supervised by Thomas Guilmeau and Emilie Chouzenoux

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 Education

• Thomas Guilmeau has coordinated an animation at La fête de la science, Oct. 2023, Saclay.
• Ségolène Martin has coordinated a group of scholars at Rendez-vous des jeunes mathématiciennes et informaticiennes (RJMI), Feb. 2023, Saclay.
• Alexandre Duval has organized several local meetups in Paris about Graph Machine Learning.

International journals

• 1 articleA.Alin Achim, L.Luca Calatroni, S.Serena Morigi and G.Gabriele Scrivanti. Space-Variant Image Reconstruction via Cauchy Regularisation: Application to Optical Coherence Tomography.Signal Processing205April 2023HALDOI
• 2 articleY.Younes Belkouchi, M.Mathieu Lederlin, A.Amira Ben Afia, C.Clement Fabre, G. R.Gilbert Raymond 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. M.Antoine M. Khalil, A.Alain Luciani, A.Anne Cotten, J. F.Jean François Meder, H.Hugues Talbot and N.Nathalie Lassau. Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge.Diagnostic and Interventional Imaging104102023, 485-489HALDOIback to text
• 3 articleY.Younes Belkouchi, H.Hugues Talbot, N.Nathalie Lassau, L.Littisha Lawrance, S.Siham Farhane, R.Rahma Feki-Mkaouar, J.Joya Hadchiti, L.Lama Dawi, J.Julien Vibert, P.-H.Paul-Henry Cournède, C.Clara Cousteix, C.Camille Mazza, M.Michele Kind, A.Antoine Italiano, A.Aurelien Marabelle, S.Samy Ammari and S.Stephane Champiat. Better than RECIST and Faster than iRECIST: Defining the Immunotherapy Progression Decision Score to Better Manage Progressive Tumors on Immunotherapy.Clinical Cancer Research298April 2023, 1528-1534HALDOI
• 4 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
• 5 articleA.Adrien Bordner, T.Théodore Aouad, C. L.Clementina Lopez Medina, S.Sisi Yang, A.Anna Molto, H.Hugues Talbot, M.Maxime Dougados and A.Antoine Feydy. A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.Diagnostic and Interventional ImagingApril 2023, 1-11HALDOIback to text
• 6 articleM.Mathieu Chalvidal, E.Emilie Chouzenoux, J.-B.Jean-Baptiste Fest and C.Claire Lefort. Block delayed Majorize-Minimize subspace algorithm for large scale image restoration.Inverse Problems394March 2023, 044002HALDOIback to textback to textback to text
• 7 articleS.Stefania Chounta, R. S.Rodrigue S. Allodji, M.Maria Vakalopoulou, M.Mahmoud Bentriou, D. T.Duyen Thi Do, F.Florent de Vathaire, I. S.Ibrahima Sory Diallo, B. C.Brice C. Fresneau, T.Thibaud Charrier, V.Vincent Zossou, S.Stergios Christodoulidis, S.Sarah Lemler and V.Véronique Letort Le Chevalier. Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer.Cancers15122023HALDOI
• 8 articleE.Emilie Chouzenoux, A.Andrés Contreras, J.-C.Jean-Christophe Pesquet and M.M. Savanier. Convergence Results for Primal-Dual Algorithms in the Presence of Adjoint Mismatch.SIAM Journal on Imaging Sciences161March 2023, 1-34HALDOIback to textback to text
• 9 articleE.Emilie Chouzenoux and J.-B.Jean-Baptiste Fest. Convergence Analysis of Block Majorize-Minimize Subspace Approach.Optimization Letters2023HALDOIback to text
• 10 articleV.Vincent Couteaux, C.Cheng Zhang, S.Sébastien Mulé, L.Laurent Milot, P.-J.Pierre-Jean Valette, C.Caroline Raynaud, A. S.Anna Sesilia Vlachomitrou, C.Cybele Ciofolo-Veit, L.Littisha Lawrance, Y.Younes Belkouchi, V.Valérie Vilgrain, M.Maité Lewin, H.Hervé Trillaud, C.Christine Hoeffel, V.Valérie Laurent, S.Samy Ammari, E.Eric Morand, O.Orphee Faucoz, A.Arthur Tenenhaus, H.Hugues Talbot, A.Alain Luciani, N.Nathalie Lassau and C.Carole Lazarus. Synthetic MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative adversarial networks.Diagnostic and Interventional Imaging10452023, 243-247HALDOIback to text
• 11 articleT.Tassnim Dardouri, M.Mounir Kaaniche, A.Amel Benazza-Benyahia, G.Gabriel Dauphin and J.-C.Jean-Christophe Pesquet. Joint Learning of Fully Connected Network Models in Lifting Based Image Coders.IEEE Transactions on Image Processing332023, 134-148HALDOIback to text
• 12 articleP.Pierre Decazes, S.Samy Ammari, Y.Younes Belkouchi, L.Léo Mottay, L.Littisha Lawrance, A.Antoine de Prévia, H.Hugues Talbot, S.Siham Farhane, P.-H. P.Paul-Henry P.-H. Cournède, A.Aurelien Marabelle, F.Florian Guisier, D.David Planchard, T.Tony Ibrahim, C.Caroline Robert, F.Fabrice Barlesi, P.Pierre Vera and N.Nathalie Lassau. Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer.Journal for Immunotherapy of Cancer119September 2023, e007315HALDOIback to text
• 13 articleC.Cédric Delmon, E.Erwan Ferrandon, E.Emilie Chouzenoux, A.Audrey Prorot, S.Sophie Alain and C.Claire Lefort. A computational two-photon fluorescence approach for revealing label-free the 3D image of viruses and bacteria.Journal of Biophotonics1652023, 9HALDOIback to textback to text
• 14 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 ImagingOctober 2023HALDOIback to text
• 15 articleV.Víctor Elvira, E.Emilie Chouzenoux, Ö. D.Ömer Deniz Akyildiz and L.Luca Martino. Gradient-based adaptive importance samplers.Journal of The Franklin Institute36013September 2023, 9490-9514HALDOIback to text
• 16 articleM.Mouna Gharbi, E.Emilie Chouzenoux and J.-C.Jean-Christophe Pesquet. An Unrolled Half-Quadratic Approach for Sparse Signal Recovery in Spectroscopy.Signal ProcessingDecember 2023HALback to textback to text
• 17 articleY.Yi Gu, Y.Yoshito Otake, K.Keisuke Uemura, M.Mazen Soufi, M.Masaki Takao, H.Hugues Talbot, S.Seiji Okada, N.Nobuhiko Sugano and Y.Yoshinobu Sato. Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography.Medical Image Analysis90December 2023, 102970HALDOI
• 18 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
• 19 articleP.Pooja Gupta, A.Angshul Majumdar, E.Emilie Chouzenoux and G.Giovanni Chierchia. DeConDFFuse: Predicting drug-drug interaction using joint deep convolutional transform learning and decision forest fusion framework.Expert Systems with Applications2023, 22HALDOIback to textback to textback to text
• 20 articleY.Yunshi Huang, E.Emilie Chouzenoux, V.Víctor Elvira and J.-C.Jean-Christophe Pesquet. Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling.Journal of The Franklin Institute36016September 2023, 12125-12149HALDOIback to textback to textback to text
• 21 articleS.Stuti Jain, E.Emilie Chouzenoux, K.Kriti Kumar and A.Angshul Majumdar. Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction.IEEE Journal of Biomedical and Health Informatics275May 2023, 2565-2574HALDOIback to textback to textback to text
• 22 articleB.Bertrand Kerautret, P.Phuc Ngo, N.Nicolas Passat, H.Hugues Talbot and C.Clara Jaquet. OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees.Image Processing On Line132023, 258-279HALDOIback to text
• 23 articleF.Fernanda Mosele, E.Elise Deluche, A.Amelie Lusque, L.Loïc Le Bescond, T.Thomas Filleron, Y.Yoann Pradat, A.Agnes Ducoulombier, B.Barbara Pistilli, T.Thomas Bachelot, F.Frederic Viret, C.Christelle Levy, N.Nicolas Signolle, A.Alexia Alfaro, D. T.Diep T N Tran, I. J.Ingrid Judith Garberis, H.Hugues Talbot, S.Stergios Christodoulidis, M.Maria Vakalopoulou, N.Nathalie Droin, A.Aurelie Stourm, M.Maki Kobayashi, T.Tomoya Kakegawa, L.Ludovic Lacroix, P.Patrick Saulnier, B.Bastien Job, M.Marc Deloger, M.Marta Jimenez, C.Celine Mahier, V.Vianney Baris, P.Pierre Laplante, P.Patricia Kannouche, V.Virginie Marty, M.Magali Lacroix-Triki, V.Veronique Diéras and F.Fabrice André. Trastuzumab deruxtecan in metastatic breast cancer with variable HER2 expression: the phase 2 DAISY trial.Nature Medicine298July 2023, 2110 - 2120HALDOIback to text
• 24 articleS.Sébastien Mulé, L.Littisha Lawrance, Y.Younes Belkouchi, V.Valérie Vilgrain, M.Maité Lewin, H.Hervé Trillaud, C.Christine Hoeffel, V.Valérie Laurent, S.Samy Ammari, E.Eric Morand, O.Orphée Faucoz, A.Arthur Tenenhaus, A.Anne Cotten, J.-F.Jean-François Meder, H.Hugues Talbot, A.Alain Luciani and N.Nathalie Lassau. Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.Diagnostic and Interventional Imaging10412023, 43-48HALDOI
• 25 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 Science2024HALback to text
• 26 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 TechnologyDecember 2023HALback to textback to text
• 27 articleM.M. Savanier, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and C.Cyril Riddell. Deep Unfolding of the DBFB Algorithm With Application to ROI CT Imaging With Limited Angular Density.IEEE Transactions on Computational Imaging92023, 502-516HALDOIback to textback to text
• 28 articleS.Shalini Sharma, A.Angshul Majumdar, E.Emilie Chouzenoux and V.Víctor Elvira. Deep State-Space Model for Predicting Cryptocurrency Price.Information SciencesNovember 2023HALback to text
• 29 articleS.Sagar Verma, N.Nicolas Henwood, M.Marc Castella, A. K.Al Kassem Jebai and J.-C.Jean-Christophe Pesquet. Neural speed–torque estimator for induction motors in the presence of measurement noise.IEEE Transactions on Industrial Electronics701January 2023, 167 - 177HALDOIback to text
• 30 articleP.Paul Zheng, E.Emilie Chouzenoux and L.Laurent Duval. PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes.IEEE Signal Processing Letters30March 2023, 215-219HALDOIback to textback to text

Invited conferences

• 31 inproceedingsV.Víctor Elvira, E.Emilie Chouzenoux, J.Jordi Cerda and G.Gustau Camps-Valls. Graphs in State-Space Models for Granger Causality in Climate Science.Colloquium 2023 - When Causal Inference Meets Statistical AnalysisParis, FranceApril 2023HALback to textback to text
• 32 inproceedingsO.Othmane Laousy, A.Alexandre Araujo, G.Guillaume Chassagnon, M.-P.Marie-Pierre Revel, S.Siddharth Garg, F.Farshad Khorrami and M.Maria Vakalopoulou. Towards Better Certified Segmentation via Diffusion Models.Proceedings of Machine Learning ResearchUAI 2023 - The 39th Conference on Uncertainty in Artificial Intelligence -216112Pittsburgh (Pennsylvania), United StatesJuly 2023, 1185–1195HALback to text
• 33 inproceedingsC.Cécile de Valle, E.Ezequiel Centofanti, E.Emilie Chouzenoux and J.-C.Jean-Christophe Pesquet. Stability of Unfolded Forward-Backward to Perturbations in Observed Data.Proceedings of the 31st European Signal Processing Conference (EUSIPCO 2023)EUSIPCO 2023 - 31st European Signal Processing ConferenceHelsinki, FinlandSeptember 2023HAL

International peer-reviewed conferences

• 34 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
• 35 inproceedingsA.Alvaro Carbonero, A.Alexandre Duval, V.Victor Schmidt, S.Santiago Miret, A.Alex Hernandez-Garcia, Y.Yoshua Bengio and D.David Rolnick. On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions.37th Conference on Neural Information Processing Systems (NeurIPS 2023) - AI4MAt workshopNew Orleans (Louisiana), United StatesDecember 2023HAL
• 36 inproceedingsJ. A.John A Castro-Correa, J. H.Jhony H Giraldo, A.Anindya Mondal, M.Mohsen Badiey, T.Thierry Bouwmans and F. D.Fragkiskos D. Malliaros. Time-varying Signals Recovery via Graph Neural Networks.ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal ProcessingRhodes Island, Greece2023, 1-5HALDOIback to textback to text
• 37 inproceedingsE.Emilie Chouzenoux and V.Víctor Elvira. GRAPHIT: Iterative reweighted l1 algorithm for sparse graph inference in state-space models.ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal ProcessingRhodes (Grèce), GreeceIEEE2023HALDOIback to textback to text
• 38 inproceedingsC.Clément Cosserat, B.Ben Gabrielson, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and T.Tülay Adali. A proximal approach to IVA-G with convergence guarantees.ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal ProcessingProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)Rhodes (Grèce), GreeceJune 2023HALDOI
• 39 inproceedingsA.Alexandre Duval, V.Victor Schmidt, A.Alex Hernandez, S.Santiago Miret, F. D.Fragkiskos D. Malliaros, Y.Yoshua Bengio and D.David Rolnick. FAENet: Frame Averaging Equivariant GNN for Materials Modeling.ICML 2023 - International Conference on Machine LearningHawaii, United StatesJuly 2023HALback to textback to textback to text
• 40 inproceedingsJ.-B.Jean-Baptiste Fest, A.Audrey Repetti and E.Emilie Chouzenoux. A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent.Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023)MLSP 2023 - IEEE International Workshop on Machine Learning for Signal ProcessingRome, ItalySeptember 2023HAL
• 41 inproceedingsJ. H.Jhony H. Giraldo, S.Sajid Javed, A.Arif Mahmood, F. D.Fragkiskos D. Malliaros and T.Thierry Bouwmans. Higher-order Sparse Convolutions in Graph Neural Networks.ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech, and Signal ProcessingRhodes Island, GreeceIEEE2023, 1-5HALDOIback to text
• 42 inproceedingsJ. H.Jhony H Giraldo, K.Konstantinos Skianis, T.Thierry Bouwmans and F. D.Fragkiskos D. Malliaros. On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks.CIKM 2023 - The 32nd ACM International Conference on Information and Knowledge ManagementBirmingham United Kingdom, United KingdomACMOctober 2023, 566-576HALDOIback to textback to textback to text
• 43 inproceedingsY.Yi Gu, Y.Yoshito Otake, K.Keisuke Uemura, M.Masaki Takao, M.Mazen Soufi, Y.Yuta Hiasa, H.Hugues Talbot, S.Seiji Okada, N.Nobuhiko Sugano and Y.Yoshinobu Sato. MSKdeX: Musculoskeletal (MSK) Decomposition from an X-Ray Image for Fine-Grained Estimation of Lean Muscle Mass and Muscle Volume.Lecture Notes in Computer ScienceMICCAI 2023 - 26th International Conference on Medical Image Computing and Computer-Assisted InterventionLNCS-14226Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Vancoucer, CanadaSpringer Nature SwitzerlandOctober 2023, 497-507HALDOIback to text
• 44 inproceedingsT.Thomas Guilmeau, E.Emilie Chouzenoux and V.Víctor Elvira. Adaptive simulated annealing through alternating Renyi divergence minimization.ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal ProcessingProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)Rhodes (Grèce), Greece2023HALDOIback to text
• 45 inproceedingsA.Alex Hernandez-Garcia, A.Alexandre Duval, A.Alexandra Volokhova, Y.Yoshua Bengio, D.Divya Sharma, P. L.Pierre Luc Carrier, M.Michał Koziarski and V.Victor Schmidt. Crystal-GFN: sampling crystals with desirable properties and constraints.37th Conference on Neural Information Processing Systems (NeurIPS 2023) - AI4MAt workshopProceedings of NeurIPS 2023New Orleans (Louisiana), United StatesDecember 2023HAL
• 46 inproceedingsH.Hitesh Jain, S.Sagar Verma and S.Siddharth Gupta. Investigating Large Vision Model Training Challenges on Satellite Datasets.InGARSS 2023 - India Geoscience and Remote Sensing SymposiumBengaluru, IndiaDecember 2023HAL
• 47 inproceedingsO.Othmane Laousy, A.Alexandre Araujo, G.Guillaume Chassagnon, N.Nikos Paragios, M. P.Marie Pierre Revel and M.Maria Vakalopoulou. Certification of Deep Learning Models for Medical Image Segmentation.Lecture Notes in Computer Science book series (LNCS,volume 14223)MICCAI 2023 - 26th International Conference on Medical Image Computing and Computer Assisted Intervention14223Vancouver (BC), CanadaOctober 2023, 611–621HALDOIback to text
• 48 inproceedingsS.Sonia Martinot, N.Nikos Komodakis, M.Maria Vakalopoulou, N.Norbert Bus, C.Charlotte Robert, E.Eric Deutsch and N.Nikos Paragios. Differentiable Gamma Index-Based Loss Functions: Accelerating Monte-Carlo Radiotherapy Dose Simulation.Information Processing in Medical Imaging13939Lecture Notes in Computer ScienceSan Carlos De Bariloche, ArgentinaSpringer Nature SwitzerlandJune 2023, 485-496HALDOIback to text
• 49 inproceedingsL.Leo Milecki, V.Vicky Kalogeiton, S.Sylvain Bodard, D.Dany Anglicheau, J.-M.Jean-Michel Correas, M.-O.Marc-Olivier Timsit and M.Maria Vakalopoulou. MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation.Proceedings of Machine Learning ResearchMIDL 2023 - Medical Imaging with Deep LearningNashville (Tennessee), United StatesJuly 2023HAL
• 50 inproceedingsJ.J. Nicolaes, E.E. Tselenti, T.T. Aouad, C.C. López-Medina, A.A. Feydy, H.Hugues Talbot, B.B. Hoepken, N.N. de Peyrecave and M.M. Dougados. POS0341 PERFORMANCE ANALYSIS OF A DEEP LEARNING ALGORITHM TO DETECT POSITIVE SIJ MRI ACCORDING TO THE ASAS DEFINITION IN AXSPA PATIENTS.EULAR 2023 European Congress of Rheumatology,Milan, ItalyBMJ Publishing Group Ltd and European League Against RheumatismMay 2023, 418.1-418HALDOI
• 51 inproceedingsG.George Panagopoulos, N.Nikolaos Tziortziotis, M.Michalis Vazirgiannis and F. D.Fragkiskos D Malliaros. Maximizing Influence with Graph Neural Networks.ASONAM 2023 - IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningKusadasi, TurkeyNovember 2023HALback to text
• 52 inproceedings A.Akash Panigrahi, S.Sagar Verma, M.Matthieu Terris and M.Maria Vakalopoulou. Have Foundational Models Seen Satellite Images? IGARSS 2023 - International Geoscience and Remote Sensing Symposium Pasadena, United States May 2023 HAL
• 53 inproceedingsJ.Jérôme Rony, J.-C.Jean-Christophe Pesquet and I. B.Ismail Ben Ayed. Proximal Splitting Adversarial Attack for Semantic Segmentation.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Vancouver (CA), CanadaIEEE2023, 20524-20533HALDOIback to text
• 54 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.International Conference on Discrete Geometry and Mathematical Morphology (DGMM)Florence, Italy2024HALback to text
• 55 inproceedingsM.Matthieu Terris and S.Sagar Verma. Investigating Model Robustness Against Sensor Variation.IGARSS 2023 - International Geoscience and Remote Sensing SymposiumPasadena, United StatesJuly 2023HAL
• 56 inproceedingsS.Sagar Verma and K.Kavya Gupta. Post Wildfire Burnt-up Detection using Siamese UNet.ECML PKDD 2023 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in DatabasesTurin, ItalySeptember 2023HAL
• 57 inproceedingsJ.Jingwei Zhang, S.Saarthak Kapse, K.Ke Ma, P.Prateek Prasanna, J.Joel Saltz, M.Maria Vakalopoulou and D.Dimitris Samaras. Prompt-MIL: Boosting Multi-instance Learning Schemes via Task-Specific Prompt Tuning.Lecture Notes in Computer ScienceMICCAI 2023 - 26th International Conference on Medical Image Computing and Computer Assisted InterventionLNCS-14227Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Vancouver, CanadaSpringer Nature SwitzerlandOctober 2023, 624-634HALDOIback to text
• 58 inproceedingsJ.Jingwei Zhang, K.Ke Ma, S.Saarthak Kapse, J.Joel Saltz, M.Maria Vakalopoulou, P.Prateek Prasanna and D.Dimitris Samaras. SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology.Lecture Notes in Computer ScienceMICCAI 2023 - Medical Image Computing and Computer Assisted InterventionLNCS-14393Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 WorkshopsVancouver, CanadaSpringer Nature SwitzerlandDecember 2023, 161-170HALDOIback to text
• 59 inproceedingsP.Paul Zheng, E.Emilie Chouzenoux and L.Laurent Duval. Démélange, déconvolution et débruitage conjoints d’un modèle convolutif parcimonieux avec dérive instrumentale, par pénalisation de rapports de normes ou quasi-normes lissées (PENDANTSS).GRETSI 2023Grenoble, FranceJuly 2023HAL

Conferences without proceedings

• 60 inproceedingsY.Y. Belkouchi, L.L. Dawi, L.L. Lawrance, S.S. Ammari, D.D. Vasseur, F.F. Wirth, O.O. Gautier, P.-H.P-H. Cournede, J.J. Hadchiti, C.C. David, F.F. Bidault, C.C. Balleyguier, M.M. Kind, H.Hugues Talbot, A.A. Italiano and N.N. Lassau. 142P Correlating total tumor volume on CT-Scan and liquid biopsy ctDNA in 1017 patients with metastatic cancer: A novel study.ESMO Congress 202334Madrid, SpainOctober 2023, S237HALDOIback to text
• 61 inproceedingsL.L. Dawi, S.S. Ammari, Y.Younes Belkouchi, J.J. Hadchiti, L.L. Lawrance, F.F. Wirth, A.Aline Bertin, S.S. Morer, N.N. Billet, C.C. Balleyguier, P.-H.P-H. Cournede, H.Hugues Talbot and N.N. Lassau. 1022MO Predicting overall survival of patients with melanoma and NSCLC treated with immunotherapy using AI combining total tumor volume and tumor heterogeneity on CT-Scans.ESMO Congress 202334Madrid, SpainOctober 2023, S622HALDOIback to text
• 62 inproceedingsB.Bertrand Kerautret, P.Phuc Ngo, N.Nicolas Passat, H.Hugues Talbot, C.Clara Jaquet and A.Adam Germain. OpenCCO : Une implémentation de l’algorithme CCO pour la construction d’arbres vasculaires 2D et 3D.Atelier R-Vessel-X 2023Lyon, FranceJune 2023HAL
• 63 inproceedingsA.Arnaud Quillent, S.Samy Ammari, É.Émilie Chouzenoux, V.Víctor Elvira and N.Nathalie Lassau. Machine-learning based guided diagnosis of parotid tumours from MRI.IEEE International Symposium on Biomedical Imaging (ISBI 2023)Cartagena (Colombia), ColombiaApril 2023HAL

Scientific book chapters

• 64 inbookE.Emilie Chouzenoux and J.-C.Jean-Christophe Pesquet. Optimization Methods for Signal Processing.Source Separation in Physical-Chemical SensingChapter 2IEEE Press; JOHN WILEY & SONSOctober 2023HALback to text

Reports & preprints

• 65 miscJ.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.November 2023HALback to text
• 66 miscS.Sayantika Chatterjee, A.Angshul Majumdar and E.Emilie Chouzenoux. Antibiotic Bacteria Interaction: Dataset and Benchmarking.January 2024HALback to text
• 67 miscE.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.October 2023HALback to text
• 68 miscE.Emilie Chouzenoux and V.Víctor Elvira. Sparse Graphical Linear Dynamical Systems.2023HALback to textback to text
• 69 miscE.Emilie Chouzenoux, J.-B.Jean-Baptiste Fest and A.Audrey Repetti. A Kurdyka-Lojasiewicz property for stochastic optimization algorithms in a non-convex setting.February 2023HALDOI
• 70 miscE.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and F.Fernando Roldán. Solution of Mismatched Monotone+Lipschitz Inclusion Problems.2023HALDOIback to text
• 71 miscP.Pedro Gajardo, T.Thomas Guilmeau and C.Cristopher Hermosilla. Sensitivity Analysis of the Set of Sustainable Thresholds.June 2023HAL
• 72 miscM.Mouna Gharbi, S.Silvia Villa, E.Emilie Chouzenoux and J.-C.Jean-Christophe Pesquet. Unrolled deep networks for sparse signal restoration.2023HALback to text
• 73 miscT.Thomas Guilmeau and A.Alain Rapaport. Multiplicity of periodic orbits with coexistence in the chemostat subject to periodic removal rate.February 2023HAL
• 74 miscK.Kavya Gupta and S.Sagar Verma. CertViT: Certified Robustness of Pre-Trained Vision Transformers.January 2023HAL
• 75 miscL.Loïc Le Bescond, M.Maria Vakalopoulou, S.Stergios Christodoulidis, F.Fabrice Andre and H.Hugues Talbot. On the detection of Out-Of-Distribution samples in Multiple Instance Learning.December 2023HALback to text
• 76 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 2024HALback to text
• 77 miscN. L.Nora Leïla Ouzir, F.Frédéric Pascal and J.-C.Jean-Christophe Pesquet. Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions.December 2023HAL
• 78 miscA.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 cance.November 2023HAL
• 79 miscS.Shalini Sharma, É.Émilie Chouzenoux, V.Víctor Elvira and A.Angshul Majumdar. A dynamical model for stock forecasting via deep recurrent dictionary learning.December 2023HALback to text
• 80 miscS.Sagar Verma, S.Siddharth Gupta and K.Kavya Gupta. Synthetix: Pipeline for Synthetic Geospatial Data Generation.2023HAL
• 81 miscM.Mathieu Vu, E.Emilie Chouzenoux, J.-C.Jean-Christophe Pesquet and I. B.Ismail Ben Ayed. Aggregated f -average Neural Network for Interpretable Ensembling.October 2023HALDOIback to text