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

Olivier Rukundo

doi:10.1117/12.2601751

9 October 2021 Effect of the regularization hyperparameter on deep-learning-based segmentation in LGE-MRI

Olivier Rukundo

Proceedings Volume 11897, Optoelectronic Imaging and Multimedia Technology VIII; 1189717 (2021) https://doi.org/10.1117/12.2601751
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

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 LGEMRI 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

Citation Download Citation

Olivier Rukundo "Effect of the regularization hyperparameter on deep-learning-based segmentation in LGE-MRI", Proc. SPIE 11897, Optoelectronic Imaging and Multimedia Technology VIII, 1189717 (9 October 2021); https://doi.org/10.1117/12.2601751

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