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Cardiac Magnetic Resonance (CMR) is the most effective tool for theassessment and diagnosis of a heart condition, which malfunction is the world'sleading cause of death. Software tools leveraging Artificial Intelligencealready enhance radiologists and cardiologists in heart condition assessmentbut their lack of transparency is a problem. This project investigates if it ispossible to discover concepts representative for different cardiac conditionsfrom the deep network trained to segment crdiac structures: Left Ventricle(LV), Right Ventricle (RV) and Myocardium (MYO), using explainability methodsthat enhances classification system by providing the score-based values ofqualitative concepts, along with the key performance metrics. With introductionof a need of explanations in GDPR explainability of AI systems is necessary.This study applies Discovering and Testing with Concept Activation Vectors(D-TCAV), an interpretaibilty method to extract underlying features importantfor cardiac disease diagnosis from MRI data. The method provides a quantitativenotion of concept importance for disease classified. In previous studies, thebase method is applied to the classification of cardiac disease and providesclinically meaningful explanations for the predictions of a black-box deeplearning classifier. This study applies a method extending TCAV with aDiscovering phase (D-TCAV) to cardiac MRI analysis. The advantage of the D-TCAVmethod over the base method is that it is user-independent. The contribution ofthis study is a novel application of the explainability method D-TCAV forcardiac MRI anlysis. D-TCAV provides a shorter pre-processing time forclinicians than the base method.

Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset