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

Source: [Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI](<https://www.sota2.com/research/paper/68fa6aa51e209131cea296b6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:30:26.199Z

Authors: Olivier Rukundo

Published: 2020-12-10T13:35:40.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.05661>)

[PDF](<https://arxiv.org/pdf/2012.05661.pdf>)