Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset

Adrianna Janik; Jonathan Dodd; Georgiana Ifrim; Kris Sankaran; Kathleen Curran

doi:10.1117/12.2582227

15 February 2021 Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset

Adrianna Janik, Jonathan Dodd, Georgiana Ifrim, Kris Sankaran, Kathleen Curran

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159636 (2021) https://doi.org/10.1117/12.2582227
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

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 Artficial 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 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. 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 analysis. D-TCAV provides a shorter pre-processing time for clinicians than the base method.

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Adrianna Janik, Jonathan Dodd, Georgiana Ifrim, Kris Sankaran, and Kathleen Curran "Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159636 (15 February 2021); https://doi.org/10.1117/12.2582227

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