Projektbeschreibung
Innovativer Ansatz für maschinelles Lernen in der medizinischen Bildgebung
Die medizinische Bildgebung bietet eine unübertroffene Möglichkeit, Krankheiten mit hoher Genauigkeit zu erkennen, was zu einer hohen Nachfrage nach Bildgebung mit automatischer Diagnoseunterstützung für Fachkräfte im Gesundheitswesen führt. Es wird erwartet, dass maschinelles Lernen eine algorithmische Lösung für die Diagnoseautomatisierung bietet. Die Maschinen sollten jedoch in der Lage sein, neue Bilder auf der Grundlage des Wissens über die gesunde Anatomie und die zu erwartende physiologische Variabilität zu analysieren. Das Team des vom Europäischen Forschungsrat finanzierten Projekts MIA-NORMAL wird das normative Repräsentationslernen als neuartigen Deep-Learning-Ansatz für die medizinische Bildgebung entwickeln und betroffenenspezifische computergestützte Werkzeuge für die Bestätigung der Normalität, die Kontrolle der Bildqualität, das Screening und die Prävention von Krankheiten bereitstellen. Mithilfe der daraus resultierenden Modelle sollen klinisch nützliche Informationen aus den Daten gesunder Menschen gewonnen werden, und zwar so häufig wie möglich während ihrer Versorgung.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
91054 Erlangen
Deutschland