# Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging

arXiv: https://arxiv.org/abs/2503.18151 · HTML: https://mlsanity.com/paper/2503.18151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks](https://mlsanity.com/paper/1907.04595/) (2019)
- [Mechanic: A Learning Rate Tuner](https://mlsanity.com/paper/2306.00144/) (2023)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training](https://mlsanity.com/paper/2202.07993/) (2022)
- [Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification](https://mlsanity.com/paper/2308.05770/) (2023)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Cyclical Log Annealing as a Learning Rate Scheduler](https://mlsanity.com/paper/2403.14685/) (2024)

## Abstract (arXiv)

Deep learning has emerged as the predominant solution for classifying medical images. We intend to apply these developments to the ultra-widefield (UWF) retinal imaging dataset. Since UWF images can accurately diagnose various retina diseases, it is very important to clas sify them accurately and prevent them with early treatment. However, processing images manually is time-consuming and labor-intensive, and there are two challenges to automating this process. First, high perfor mance usually requires high computational resources. Artificial intelli gence medical technology is better suited for places with limited medical resources, but using high-performance processing units in such environ ments is challenging. Second, the problem of the accuracy of colour fun dus photography (CFP) methods. In general, the UWF method provides more information for retinal diagnosis than the CFP method, but most of the research has been conducted based on the CFP method. Thus, we demonstrate that these problems can be efficiently addressed in low performance units using methods such as strategic data augmentation and model ensembles, which balance performance and computational re sources while utilizing UWF images.