Scientific Challenge : BioMedical Image Understanding
The major scope of our research concerns high-content biomedical image analysis, understanding and synthesis, by actively include explicit and implicit biomedical knowledge. This challenge deals with a tripolar paradigm considering cognitive vision (in cognito) applied to biomedical images (in- & ex-vivo) in the framework of the virtual physiological human (in silico) :
In Cognito – in Silico – in Vivo BioMedical Image Analysis, Understanding and Synthesis
Multiscale and multimodal bioimage analysis / synthesis, as explainable / responsible artificial intelligence constitute the core competence necessary to this approach. Prognosis traceability and uncertainty management are considered as important elements enabling a constant implication of our medical partners and an effective translational approach. Integrating micro and macro modalities in the next generation of PACS and metadata management platforms, necessitates to go from the semiologic approaches towards operational morphogenetic models, by considering morphological, bio-physical and genetic aspects. This necessitates a close collaboration within multidisciplinary teams/projects.
Keywords: BioMedical Image Analysis, BioMedical Image Understanding, Multimodal Medical Image Fusion, Content-Based Medical Image Indexing and Retrieval, Diagnosis and Prognosis.
Translational challenges related to the proposed themes
Our scientific challenges can be grouped in three categories, related to partcular clinical challenges, all leaded in collaboration with local hospitals :
- High-content biomedical images understanding
- Breast cancer grading and visual reasoning for digital histopathology
- 3D stem cells traking
- 3D neurite tracing
- Medical image conceptualization for early detection, diagnosis and prognosis.
- Multimodal image indexing & analysis for Parkinson’s Disease prognosis.
