Expanding the deep-learning model to diagnosis LVNC: limitations and trade-offs-Reference-Cited by-同舟云学术

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Expanding the deep-learning model to diagnosis LVNC: limitations and trade-offs

Published:2024-02-11 Issue:1 Volume:12 Page:
ISSN:2168-1163
Container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Author:

Bernabé G.¹^ORCID,González-Férez P.¹^ORCID,García J. M.¹^ORCID,Casas G.²^ORCID,González-Carrillo J.³^ORCID

Affiliation:

1. Computer Engineering Department, University of Murcia, Murcia, Spain

2. Cardiology, Hospital Universitari Vall d’Hbron, Barcelona, Spain

3. Cardiology, Hospital Virgen de la Arrixaca, Murcia, Spain

Funder

Ministerio de Ciencia e Innovación

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681163.2024.2314566

Reference31 articles.

1. Left Ventricular Noncompaction

2. Deep Learning–based Automated Segmentation of Left Ventricular Trabeculations and Myocardium on Cardiac MR Images: A Feasibility Study

3. The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks

4. Bernabé G, Casanova JD, Casas G, González-Carrillo J 2020. A highly accurate method for quantifying LVNC cardiomyophaty. AMIA 2020 Annual Symposium; Chicago, USA. p. 223–232.

5. A self-optimized software tool for quantifying the degree of left ventricle hyper-trabeculation

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