Description du projet
Une approche innovante de l’apprentissage automatique pour l’imagerie médicale
L’imagerie médicale dispose d’une capacité inégalée à identifier les maladies avec une grande précision, ce qui suscite une forte demande d’imagerie avec aide au diagnostic automatisé pour les professionnels de la santé et les soignants. L’apprentissage automatique devrait fournir une solution algorithmique pour l’automatisation des diagnostics. Cependant, les machines doivent pouvoir analyser les nouvelles images en fonction des connaissances sur l’anatomie saine et la variabilité physiologique attendue. Le projet MIA-NORMAL, financé par le CER, développera l’apprentissage par représentation normative en tant que nouvelle approche d’apprentissage profond pour l’imagerie médicale, fournissant des outils informatiques spécifiques aux patients pour la confirmation de la normalité, le contrôle de la qualité des images, le dépistage sanitaire et la prévention des maladies. Les modèles qui en résulteront recueilleront des informations cliniquement utiles à partir des données de patients sains, aussi souvent que possible au cours du parcours du patient.
Objectif
As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialised clinical experts and is thus rarely available at the front-line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding certain conditions or confirming physiological normality would be essential at many stages of the patient journey, to streamline referrals and relieve pressure on human experts who have limited capacity. Hence, there is a strong need for increased imaging with automated diagnostic support for clinicians, healthcare professionals, and caregivers.
Machine learning is expected to be an algorithmic panacea for diagnostic automation. However, despite significant advances such as Deep Learning with notable impact on real-world applications, robust confirmation of normality is still an unsolved problem, which cannot be addressed with established approaches.
Like clinical experts, machines should also be able to verify the absence of pathology by contrasting new images with their knowledge about healthy anatomy and expected physiological variability. Thus, the aim of this proposal is to develop normative representation learning as a new machine learning paradigm for medical imaging, providing patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. We will do this by developing novel Deep Learning approaches that can learn without manual labels from healthy patient data only, applicable to cross-sectional, sequential, and multi-modal data. Resulting models will be able to extract clinically useful and actionable information as early and frequent as possible during patient journeys.
Champ scientifique
- medical and health sciencesbasic medicineanatomy and morphology
- social sciencessociologyindustrial relationsautomation
- medical and health sciencesbasic medicinepathology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologymedical engineeringdiagnostic imaging
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
91054 Erlangen
Allemagne