S. Jégou

发表

Brain age prediction of healthy subjects on anatomic MRI with deep learning : going beyond with an “explainable AI” mindset

G. Wainrib, T. Clozel, P. Hérent, 2018, bioRxiv.

AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients

G. Wainrib, J. Pesquet, E. Pronier, 2020, medRxiv.

Detection and characterization of MRI breast lesions using deep learning.

P. Hérent, S. Jégou, C. Balleyguier, 2019, Diagnostic and interventional imaging.

Diagnosis of focal liver lesions from ultrasound using deep learning.

A. Luciani, P. Hérent, S. Jégou, 2019, Diagnostic and interventional imaging.

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Judy H. Cho, John P. Ferguson, G. Wainrib, 2019, Scientific Reports.

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer

A. Luciani, S. Jégou, E. Itti, 2020, European Radiology.

AI-based multi-modal integration (ScanCov scores) of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients

Fabien, M. Sefta, T. Clozel, 2020 .

ToxicBlend: Virtual screening of toxic compound with ensemble predictors

Eric W. Tramel, Simon Jégou, M. Zaslavskiy, 2018, Toxicology Letters.

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Yoshua Bengio, Adriana Romero, Michal Drozdzal, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Judy H. Cho, John P. Ferguson, G. Wainrib, 2019, Scientific Reports.

ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors pdf

Eric W. Tramel, Gilles Wainrib, Mikhail Zaslavskiy, 2018, Computational Toxicology.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

G. Wainrib, J. Pesquet, E. Pronier, 2021, Nature Communications.

Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers pdf

Olivier Dehaene, Olivier Moindrot, Charlie Saillard, 2021, COMPAY@MICCAI.

Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.

S. Jégou, C. Bodet-Milin, F. Kraeber-Bodéré, 2020, European Journal of Nuclear Medicine and Molecular Imaging.

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment.

O. Lucidarme, S. Jegou, A. Luciani, 2020, Diagnostic and interventional imaging.

Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images pdf

Olivier Moindrot, Jean-Baptiste Schiratti, Simon Jégou, 2021, ArXiv.

Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks

H. Greenspan, Jean-Baptiste Schiratti, S. Jégou, 2023, European Radiology.

The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

Abel Díaz Berenguer, R. Lienhart, N. Paragios, 2023, ArXiv.

Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors.

M. Ronot, S. Jégou, M. Pavel, 2023, Future Oncology.

International Inflammatory Bowel Disease Genetics Consortium (IIBDGC)

J. Hugot, S. Jégou, K. Steen, 2019 .