D-TCAV provides a shorter pre-processing time for clinicians than the base method.Ĭardiovascular disease is the most common cause of death worldwide. The contribution of this study is a novel application of the explainability method D-TCAV for cardiac MRI analysis. The advantage of the D-TCAV method over the base method is that it is user-independent. This study applies a method extending TCAV with a Discovering phase (D-TCAV) to cardiac MRI analysis. 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. The method provides a quantitative notion of concept importance for disease classified. 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. With introduction of a need of explanations in GDPR explainability of AI systems is necessary. This project investigates if it is possible to discover concepts representative for different cardiac conditions from the deep network trained to segment cardiac 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. Software tools leveraging Artficial Intelligence already enhance radiologists and cardiologists in heart condition assessment but their lack of transparency is a problem. I have a problem with Intel Processor Diagnostic Tool 64bit 4.1.0.27.w.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.
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