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- W3112936688 abstract "In this work, the author aims at demonstrating the extent to which the arbitrary selection of the L2 regularization hyperparameter can affect the outcome of deep learning-based segmentation in LGE-MRI. Here, arbitrary L2 regularization values are used to create different deep learning-based segmentation networks. 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 achieving the 90% validation accuracy. The experimental comparisons demonstrate that small L2 regularization values can lead to better segmentation of the myocardial boundaries." @default.
- W3112936688 created "2020-12-21" @default.
- W3112936688 creator A5070608034 @default.
- W3112936688 date "2020-12-10" @default.
- W3112936688 modified "2023-09-27" @default.
- W3112936688 title "Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI" @default.
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