论文标题
正规化超参数对LGE-MRI中深度学习分割的影响
Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI
论文作者
论文摘要
证明了随着深度学习的语义分割影响，任意选择的L2正则化高参数值在多大程度上影响语义分割的结果。演示依赖于使用任意选择的L2正则化值在小LGE-MRI数据集上训练U-NET。仅当在训练验证精度达到90％之前达到所有时期的10％时，才能手动调整或调整剩余的超参数。具有深入学习结果的语义细分是客观地和主观评估的，反对手动基础真理细分。
The extent to which the arbitrarily selected L2 regularization hyperparameter value affects the outcome of semantic segmentation with deep learning is demonstrated. Demonstrations rely on training U-net on small LGE-MRI datasets using the arbitrarily selected L2 regularization values. The remaining hyperparameters are to be manually adjusted or tuned only when 10 % of all epochs are reached before the training validation accuracy reaches 90%. Semantic segmentation with deep learning outcomes are objectively and subjectively evaluated against the manual ground truth segmentation.
