translational radiomics
The Translational Radiomics group develops innovative methods for biomedical image analysis.
Activities
A specific focus is on leveraging the richness of data available in preclinical systems to train Deep Learning models and fine tune these models for clinical applications using Transfer Learning approaches.
Olivier
Keunen
Keunen
Projects & clinical trials
Research projects
Some of the group’s research projects include:
- Systematic improvement of medical images quality using AutoEncoders
- Deep molecular imaging using multi-parametric MRI
- Automatic detection of seizures in videos of epileptic rats
- Brain tumors segmentation using Federated Learning
Illustations:
Featured team members
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Selma
Boudissa Research Engineer -
Georgia
Kanli Research Engineer -
Olivier
Keunen Group Leader -
Valérie
Palissot Scientist -
Daniele
Perlo Postdoctoral Fellow
Scientific publications
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Multi-parametric MRI to FMISO PET Synthesis for Hypoxia Prediction in Brain Tumors – 09/10/2024
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Addressing Artefacts in Anatomical MR Images – 01/05/2024
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Early-life influenza A (H1N1) infection independently programs brain connectivity, HPA AXIS and tissue-specific gene expression profiles – 11/03/2024
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Corrigendum to ‘Molecular Identity Changes of Tumor-Associated Macrophages and Microglia After Magnetic Resonance Imaging–Guided Focused Ultrasound–Induced Blood–Brain Barrier Opening in a Mouse Glioblastoma Model’ [Ultrasound in Med & Biol. 49 (2023) 1082-1090, (S0301562922006664), (10.1016/j.ultrasmedbio.2022.12.006)] – 01/01/2024
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Predicting Hypoxia in Brain Tumors from Multiparametric MRI – 25/01/2024
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Immune cell identity behind the Ktrans mapping of mouse glioblastoma – 21/06/2023
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Large-Scale Deep Learning Medical Image Methodology and Applications using Multiple GPUs – 12/10/2023
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Molecular Identity Changes of Tumor-Associated Macrophages and Microglia After Magnetic Resonance Imaging–Guided Focused Ultrasound–Induced Blood–Brain Barrier Opening in a Mouse Glioblastoma Model – 01/01/2023
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Author Correction – 26/01/2023
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Federated learning enables big data for rare cancer boundary detection – 05/12/2022
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