Supervised and Semi-Supervised Explainable AI applied to Cardiovascular Magnetic Resonance Mapping Techniques

Deep Learning techniques have demonstrated broad applicability across numerous domains, including medicine. Among these, Cardiovascular Magnetic Resonance Imaging stands out as a field where Deep Learning has the potential to transform myocardial tissue characterization through the automated, objective, and reproducible assessment of modern-day quantitative techniques like T1 and T2 mapping images, which serve as the case study of this Thesis. The clinical implementation of these techniques, however, is hindered by several obstacles, including the need for center-specific reference values -- which strongly depend on the magnetic field strength, acquisition sequence, and chosen parameters -- along with the scarcity of large annotated datasets, class imbalance, and the requirement for transparent and explainable decision-making. This Thesis proposes a Deep Learning approach that utilizes supervised and semi-supervised learning and model ensembling to navigate some of these challenges. It provides explainability tools to support transparent case-level explanation of model predictions, facilitate clinician trust and supporting clinical adoption. Experimental results demonstrate that the proposed framework enhanced predictive accuracy and reliability harnessing actionable insights in Cardiac Imaging.