Chairs "AI for Integrative Computational Medicine"

AI for e-patient & e-medicine based on statistical, geometrical, biophysical, & semantic knowledge of anatomy, physiology & metabolism.

Biophysics-based AI

  • Learning biophysical parameters for quantitative diagnosis 
  • Predicting evolution of pathologies & effect of therapies (digital twin) 
  • Data augmentation from biophysical simulation 

Medical data management 

  • Medical Data Lab (UCA Idex) 
  • Health Data Hub, EDS APHP 
  • International databases 
  • Security, privacy, GDPR 
  • In collaboration with other 3IA institutes 

Data-driven AI 

  • Imaging and omics biomarkers (genetics, transcriptomics, proteomics, metabolomics) & lifestyle, behavior, etc. for patient selection 
  • Video analytics & sensors for patient monitoring 

3IA Chair holders

 
3IA Chairs awarded in 2019

Nicholas Ayache (Inria) - AI for e-patients and e-medicine 

We are designing and exploiting modern AI methods: (i) to personalize the parameters of advanced models of the e-patient, (ii) to drive e-medicine algorithms on personalized e-patients (i.e. digital twins) for automated diagnosis, prognosis and therapy, in an efficient, robust, safe and explainable manner. We also seek to augment databases with biophysical simulation. 

François Bremond (Inria) - Video analytics for human behavior understanding 

Video analytics enables us to measure objectively the behavior of humans by recognizing their everyday activities, their emotion, eating habits and lifestyle. Human behavior can be modeled by learning from a large quantity of data from a variety of sensors to improve and optimize, for instance, the quality of life of people suffering from behavior disorders. 

Hervé Delingette (Inria) - Joint biological and imaging biomarkers in oncology 

We exploit joint information from imaging and biological data to improve the diagnosis and treatment planning, focusing on lung cancer. This approach relies on methods involving unsupervised deep learning, uncertainty quantification, sparse Bayesian feature selection and the handling of confounding factors

Rachid Deriche (Inria) - Computational brain connectomics 

This project will reconstruct and analyze the brain’s neural connections network, the connectome, via a computational brain connectomics framework based on ground-breaking AI algorithms and ML tools to gain insight into brain architecture, functioning and neurodegenerative diseases. 

Olivier Humbert (Université Côte d’Azur) - Comprehensive omics profiling for precision medicine in Oncology 

I am combining various patient extracted “omics” data, including multimodal imaging features, for integrative and data-driven computational medicine. I focus on challenging fields in oncology such as (i) radiogenomics and outcome-focused research in metastatic breast cancer and (ii) the accurate prediction of response to immunotherapy. 

Jean-Pierre Merlet (Inria) - Non-invasive assessment of disabilities 

We use mathematical/AI methods for (i) designing non-intrusive and affordable monitoring/assistance devices that are adaptable to the user’s/doctor’s needs, (ii) deducing medically pertinent health-indicators from the data, taking into account measurement errors, and (iii) detecting rare events that may be the sign of emerging pathology.

Maxime Sermesant (Inria) - AI and biophysical models for computational cardiology   

The application of AI in healthcare is challenging due to its lack of robustness and explainability. This project aims to introduce physiological priors in AI through biophysical models. This can be done by reformulating problems through such models, by learning spatiotemporal dynamics from biophysics or by augmenting features and data with such simulations.