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.

翻译：在这项工作中,作者旨在证明任意选择L2正规化超参数可在多大程度上影响LGE-MRI的深层学习分解结果。这里,任意的L2正规化值被用来创建不同的深层学习分解网络。此外,作者采用其他深层学习超参数的手工调整或调试,只有在达到所有时代的10%后才能实现90%的验证准确度。实验性比较表明,小L2正规化值可以导致心肌界限的更好分解。