Abstract
Myocardial infarction is one of the most common cardiovascular diseases. Clinical information and Delayed Enhancement cardiac MRI (DE-MRI) are crucial to diagnose the myocardial infarction. However, some discrepancies can occur between clinical characteristics and DE-MRI when the disease is diagnosed. In order to deal in an efficient way with the correlation between these data and to be able to automatically classify patients suffering from myocardial infarction, this paper proposes a mixed classification model that takes both the clinical characteristics and DE-MRI into account. In the mixed model, a 3D Convolutional Neural Network (CNN) encodes the MRI as the surface of infarction then the surface is fed with Random Forest and other clinical characteristics to make the final decision.
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Acknowledgements
This work was partly supported by the ADVANCES project founded by ISITE-BFC project (number ANR-15-IDEX-0003) and by the EIPHI Graduate School (contract ANR-17-EURE-0002). We also thank the Mesocentre of Franche-Comté for the computing facilities.
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Shi, J., Chen, Z., Couturier, R. (2021). Classification of Pathological Cases of Myocardial Infarction Using Convolutional Neural Network and Random Forest. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_43
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DOI: https://doi.org/10.1007/978-3-030-68107-4_43
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