Julia A. Schnabel, PhD
Chair in Computational Imaging
Head of Research & Impact
School of Biomedical Engineering & Imaging Sciences
King’s College London, UK
AI-Enabled Medical Imaging
Abstract:
Artificial intelligence, in particular from the class of machine learning and deep learning, has shown great promise for application in medical imaging. However, the success of AI-based techniques is limited by the availability and quality of the training data. A common approach is to train methods on well annotated and curated databases of high-quality image acquisitions, which then may fail on real patient cases in a hospital setting. Another problematic is the lack of sufficient numbers of clinical label annotations in the training data, or example for early markers of disease. In this talk I will present some of our recent approaches that aim to address some of these challenges, by using AI as an enabling technique for improved image reconstruction, realistic data augmentation and further downstream tasks. I will conclude by giving an outlook on the future opportunities in this field, operating right from the imaging sensor to extracting clinically relevant measures.