Jean-Baptiste Schiratti

发表

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

Slim Essid, Alexandre Gramfort, Michel Le Van Quyen, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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.

Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns

S. Allassonnière, Jean-Baptiste Schiratti, S. Durrleman, 2018, Front. Neurol..

Mixed-effects model for the spatiotemporal analysis of longitudinal manifold-valued data

Stanley Durrleman, Olivier Colliot, Stéphanie Allassonnière, 2015 .

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 .

An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data.

G. Wainrib, C. Maussion, M. Sefta, 2022, European journal of cancer.

A Deep Learning Method for Identifying Predictors of Knee Osteoarthritis Radiographic Progression From Baseline MRI

G. Wainrib, T. Clozel, Jean-Baptiste Schiratti, 2021 .

Learning spatiotemporal trajectories from manifold-valued longitudinal data

Stanley Durrleman, Olivier Colliot, Stéphanie Allassonnière, 2015, NIPS.

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.

A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations

Stanley Durrleman, Olivier Colliot, Stéphanie Allassonnière, 2017, J. Mach. Learn. Res..

A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data

Stanley Durrleman, Olivier Colliot, Stéphanie Allassonnière, 2015, IPMI.

A deep learning method for predicting knee osteoarthritis radiographic progression from MRI

G. Wainrib, T. Clozel, Jean-Baptiste Schiratti, 2021, Arthritis Research & Therapy.

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.

Statistical Learning of Spatiotemporal Patterns from Longitudinal Manifold-Valued Networks pdf

Stanley Durrleman, Olivier Colliot, Stéphanie Allassonnière, 2017, MICCAI.

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.

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Jean-Baptiste Schiratti, C. Saillard, Alexandre Filiot, 2023, medRxiv.

A deep learning-based multiscale integration of spatial omics with tumor morphology

Jean-Baptiste Schiratti, Benoît Schmauch, Loïc Herpin, 2024, bioRxiv.