Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging
This work addresses the problem of enabling accurate and early retinal disease diagnosis in resource-limited medical settings, though it is incremental as it applies existing deep learning techniques to a new imaging modality.
The paper tackled the challenge of automating ultra-widefield retinal image classification for disease diagnosis by addressing high computational resource demands and accuracy issues compared to color fundus photography, achieving efficient processing on low-performance units through methods like data augmentation and model ensembles.
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.