Digital Health Axis
The analysis, processing and modeling of health data are currently at the heart of a major revolution with the need to propose new digital approaches in public health and personalized medicine. The arrival of this massive data, closely linked to the development of new production methods, poses new challenges in terms of integration, representation and structuring, analysis and prediction, and relies on a diversity of methods covering a wide field of computer science, ranging from fundamental to applied research. Thanks to our various expertises, in particular in medical imaging, computational biology, knowledge representation, systems biology, dynamic modeling, and more generally in learning and artificial intelligence, we have a multidisciplinary team to work effectively on this broad theme that we call Digital Health.
The objectives of this Digital Health axis are to support this emerging dynamic on the processing of massive and highly heterogeneous data that have been processed independently within the laboratory (medical images, omics data, clinical data, etc.), to develop intra- and inter-departmental collaborations. In a structural way, this axis will also support the transverse axis "Artificial intelligence" of the LaBRI.
- Medical imaging: acquisition, reconstruction, processing, analysis
- Multimodal data analysis
- AI applied to medical imaging, explainability
- Disability assistance, prevention
- Brain-machine interface
- Integration of heterogeneous data: from multi-omics data to clinical data
- Biomarker detection
- Dynamic models of cell differentiation and reprogramming
- Inference of executable models from omics data
- Knowledge representation/li>
- Cardiac simulation
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