# Effects of Image Size on Deep Learning

arXiv: https://arxiv.org/abs/2101.11508 · HTML: https://mlsanity.com/paper/2101.11508/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Evaluation of Rounding Functions in Nearest-Neighbor Interpolation](https://mlsanity.com/paper/2003.06885/) (2020)
- [Software Implementation of Optimized Bicubic Interpolated Scan Conversion in Echocardiography](https://mlsanity.com/paper/2005.11269/) (2020)
- [Normalized Weighting Schemes for Image Interpolation Algorithms](https://mlsanity.com/paper/2011.08559/) (2020)
- [Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI](https://mlsanity.com/paper/2012.05661/) (2020)
- [Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software](https://mlsanity.com/paper/2012.09070/) (2020)

## Built on by (most cited first, 8 of 8)

- [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)
- [Beyond Nearest Neighbor Interpolation in Data Augmentation](https://mlsanity.com/paper/2504.01527/) (2025)
- [Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition](https://mlsanity.com/paper/2501.08169/) (2025)
- [Challenges of 3D Surface Reconstruction in Capsule Endoscopy](https://mlsanity.com/paper/2103.10390/) (2021)
- [Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks](https://mlsanity.com/paper/2506.23016/) (2025)
- [Artificial Intelligence in the Food Industry: Food Waste Estimation based on Computer Vision, a Brief Case Study in a University Dining Hall](https://mlsanity.com/paper/2507.14662/) (2025)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).