Descrizione del progetto
Un approccio innovativo all’apprendimento automatico per la diagnostica per immagini
La diagnostica per immagini offre una capacità ineguagliata di individuare le malattie con elevata precisione, determinando una forte domanda di immaginografia con supporto diagnostico automatizzato per gli operatori sanitari e i caregiver. L’apprendimento automatico dovrebbe fornire una soluzione algoritmica per l’automazione diagnostica. Tuttavia, le macchine dovrebbero essere in grado di analizzare le nuove immagini in base alle conoscenze sull’anatomia sana e sulla variabilità fisiologica prevista. Il progetto MIA-NORMAL, finanziato dal CER, svilupperà l’apprendimento della rappresentazione normativa come un nuovo approccio di apprendimento profondo per la diagnostica per immagini, fornendo strumenti computazionali specifici per il paziente per la conferma della normalità, il controllo della qualità delle immagini, lo screening della salute e la prevenzione delle malattie. I modelli risultanti otterranno informazioni clinicamente utili dai dati dei pazienti sani il più frequentemente possibile durante i patient journey.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
91054 Erlangen
Germania