Abstract
In this preliminary evaluation, the author demonstrates the extent to which the arbitrary selection of the L2 regularization hyperparameter can affect the outcome of deep learning-based segmentation in LGE-MRI. Also, the author adopts the manual adjustment or tunning, of other deep learning hyperparameters, to be done only when 10% of all epochs are reached before attaining the 90% validation accuracy. With the arbitrary L2 regularization values, used in the experiments, the results showed that the smaller L2 regularization number can lead to better segmentation of the myocardium and/or higher accuracy.
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URL
https://arxiv.org/abs/2012.05661