Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI

Olivier Rukundo

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arXiv · 2012.05661

Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI

Olivier Rukundo

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Abstract

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.

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Olivier Rukundo. 2020-12-10. Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI. https://arxiv.org/abs/2012.05661

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