  Cardiac Magnetic Resonance (CMR) is the most effective tool for the
assessment and diagnosis of a heart condition, which malfunction is the world's
leading cause of death. Software tools leveraging Artificial Intelligence
already enhance radiologists and cardiologists in heart condition assessment
but their lack of transparency is a problem. This project investigates if it is
possible to discover concepts representative for different cardiac conditions
from the deep network trained to segment crdiac structures: Left Ventricle
(LV), Right Ventricle (RV) and Myocardium (MYO), using explainability methods
that enhances classification system by providing the score-based values of
qualitative concepts, along with the key performance metrics. With introduction
of 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 important
for cardiac disease diagnosis from MRI data. The method provides a quantitative
notion of concept importance for disease classified. In previous studies, the
base method is applied to the classification of cardiac disease and provides
clinically meaningful explanations for the predictions of a black-box deep
learning classifier. This study applies a method extending TCAV with a
Discovering phase (D-TCAV) to cardiac MRI analysis. The advantage of the D-TCAV
method over the base method is that it is user-independent. The contribution of
this study is a novel application of the explainability method D-TCAV for
cardiac MRI anlysis. D-TCAV provides a shorter pre-processing time for
clinicians than the base method.
