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

arXiv: https://arxiv.org/abs/2012.05661 · HTML: https://mlsanity.com/paper/2012.05661/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning](https://mlsanity.com/paper/2302.11522/) (2023)
- [Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles](https://mlsanity.com/paper/2310.19630/) (2023)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

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.