103 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université Paris CitéFR15 papers
- InsermFR12 papers
- Sorbonne UniversitéFR12 papers
- Sorbonne Paris CitéFR10 papers
- Centre de Recherche des CordeliersFR9 papers
- Institut national de recherche en sciences et technologies du numériqueFR8 papers
- Assistance Publique – Hôpitaux de ParisFR7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- École PolytechniqueFR4 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR3 papers
- Hôpital Européen Georges-PompidouFR3 papers
- Infection, Anti-microbiens, Modélisation, EvolutionFR3 papers
22 papers
AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction
Fabien Maury, Solène Grosdidier, Maud de Dieuleveult +1
Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific…
Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
Pierre Epron, Adrien Coulet, Mehwish Alam
Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to pr…
Toward Valid Generative Clinical Trial Data with Survival Endpoints
Perrine Chassat, Van Tuan Nguyen, Lucas Ducrot +2
Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases…
Untangling Vascular Trees for Surgery and Interventional Radiology
Guillaume Houry, Tom Boeken, Stéphanie Allassonnière +1
The diffusion of minimally invasive, endovascular interventions motivates the development of visualization methods for complex vascular networks. We propose a planar representation…
Calibrating confounding strength in sensitivity models for weighting estimators: a comparative review and a new method
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
Causal inference is only valid when its underlying assumptions are satisfied, one of the most central being the ignorability or unconfoundedness assumption. However, this hypothesi…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Fast Large Deformation Matching with the Energy Distance Kernel
Siwan Boufadene, François-Xavier Vialard, Jean Feydy
We propose an efficient framework for point cloud and measure registration using bi-Lipschitz homeomorphisms, achieving O(n log n) complexity, where n is the number of points. By l…
In silico clinical trials in drug development: a systematic review
Bohua Chen, Lucia Chantal Schneider, Christian Röver +15
In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of th…
Wavelet-Based Multiscale Flow For Realistic Image Deformation in the Large Diffeomorphic Deformation Model Framework
Fleur Gaudfernau, Eléonore Blondiaux, Stéphanie Allassonnière +1
Estimating accurate high-dimensional transformations remains very challenging, especially in a clinical setting. In this paper, we introduce a multiscale parameterization of deform…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Straightforward Phase I Dose-Finding Design for Healthy Volunteers Accounting for Surrogate Activity Biomarkers
Sandrine Boulet, Emmanuelle Comets, Antoine Guillon +5
Conventionally, a first-in-human phase I trial in healthy volunteers aims to confirm the safety of a drug in humans. In such situations, volunteers should not suffer from any safet…
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation
Léo Machado, Hélène Philippe, Élodie Ferreres +12
Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical pr…
Sharp Bounds for Continuous-Valued Treatment Effects with Unobserved Confounders
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich +2
In causal inference, treatment effects are typically estimated under the ignorability, or unconfoundedness, assumption, which is often unrealistic in observational data. By relaxin…
Memory-Scalable and Simplified Functional Map Learning
Robin Magnet, Maks Ovsjanikov
Deep functional maps have emerged in recent years as a prominent learning-based framework for non-rigid shape matching problems. While early methods in this domain only focused on…
T-Rep: Representation Learning for Time Series using Time-Embeddings
Archibald Fraikin, Adrien Bennetot, Stéphanie Allassonnière
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this…
Bayesian Framework for Multi-Source Data Integration -- Application to Human Extrapolation From Preclinical Studies
Sandrine Boulet, Moreno Ursino, Robin Michelet +4
In preclinical investigations, e.g. in in vitro, in vivo and in silico studies, the pharmacokinetic, pharmacodynamic and toxicological characteristics of a drug are evaluated befor…
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
Clément Chadebec, Louis J. Vincent, Stéphanie Allassonnière
In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have g…
A straightforward meta-analysis approach for oncology phase I dose-finding studies
Christian Röver, Moreno Ursino, Tim Friede +1
Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about t…
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos +1
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational aut…
Data Augmentation with Variational Autoencoders and Manifold Sampling
Clément Chadebec, Stéphanie Allassonnière
We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting. This method reveals particularly well suited to perform data aug…
Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more sp…
Random-effects meta-analysis of phase I dose-finding studies using stochastic process priors
Moreno Ursino, Christian Röver, Sarah Zohar +1
Phase I dose-finding studies aim at identifying the maximal tolerated dose (MTD). It is not uncommon that several dose-finding studies are conducted, although often with some varia…