Hugues Talbot

Professor

CentraleSupélec

France

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About

Hugues Talbot is a Professor at CentraleSupelec in France. His research interests include artificial intelligence applications in medical imaging, particularly in the detection and quantification of pulmonary embolism, and developing deep learning models for various diagnostic challenges. He has contributed to advancing methodologies for evaluating treatment benefits in cancer patients and estimating bone mineral density from imaging data. His recent works emphasize the intersection of technology and healthcare, aiming to improve patient outcomes through innovative solutions.

Articles (19)

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2025

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2024

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2024

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2021

Collaborators (7)

ian paddick

Chief Physicist

UNITED KINGDOM

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Mostafa EL HAJJAM

Praticien Hospitalier - Professeur au Collège de Médecine - Ancien Professeur Associé des Universités UVSQ

Assistance Publique - Hôpitaux de Paris

FRANCE

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Paul-Henry COURNEDE

Professor

FRANCE

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Yoshinobu Sato

Professor

Nara Institute of Science and Technology（NAIST）

JAPAN

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Jamie McClelland

UNITED KINGDOM

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Nathalie Lassau

Professor

Gustave Roussy

FRANCE

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Sebastien Ourselin

University College London

UNITED KINGDOM

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Hugues Talbot

Research Interests

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About

Articles (19)

Learning Truly Monotone Operators with Applications to Nonlinear Inverse Problems

Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution

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

Better than RECIST and Faster than iRECIST: Defining the Immunotherapy Progression Decision Score to Better Manage Progressive Tumors on Immunotherapy

A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging

Detection and quantification of pulmonary embolism with artificial intelligence: The SFR 2022 artificial intelligence data challenge

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Bringing Interventional Radiology to Mars!

OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees

Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer

Synthetic MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative adversarial networks

Toward semi-automatic biologically effective dose treatment plan optimisation for Gamma Knife radiosurgery.

Prediction of Early Response to Immunotherapy: DCE-US as a New Biomarker

Predicting immunotherapy outcomes in patients with MSI tumors using NLR and CT global tumor volume

Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge

ZeBraInspector, a platform for the automated segmentation and analysis of body and brain volumes in whole 5 days post-fertilization zebrafish following simultaneous visualization with identical orientations

Distributed Algorithms for Scalable Proximity Operator Computation and Application to Video Denoising

Homotopic affine transformations in the 2D Cartesian grid

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning

Collaborators (7)

ian paddick

Mostafa EL HAJJAM

Paul-Henry COURNEDE

Yoshinobu Sato

Jamie McClelland

Nathalie Lassau

Sebastien Ourselin

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