Deploying deep learning approaches to left ventricular non-compaction measurement

Author:

Rodríguez-de-Vera Jesús M.,González-Carrillo Josefa,García José M.,Bernabé GregorioORCID

Funder

Ministerio de Ciencia, Innovación y Universidades

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Reference25 articles.

1. Berman M, Triki AR, Blaschko MB (2018) The lovasz-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

2. Bernabé G, Casanova JD, Cuenca J, González-Carrillo J (2018) A self-optimized software tool for quantifying the degree of left ventricle hyper-trabeculation. J Supercomput 75(3):1625–1640

3. Bernabé G, Cuenca J, de Teruel PEL et al (2015) A software tool for the automatic quantification of the left ventricle myocardium hyper-trabeculation degree. Procedia Comput Sci 51:610–619 (International Conference On Computational Science, ICCS 2015)

4. Bernabé G, González-Carrillo J, Cuenca J et al (2017) Performance of a new software tool for automatic quantification of left ventricular trabeculations. Revista Española de Cardiología (English Edition) 70(5):405–407

5. Bernard O, Lalande A, Zotti C et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514–2525

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