用轻量方法高效处理超广角眼底图像，适合资源有限的医疗环境。
Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging
- 结合数据增强与模型集成，在低算力设备上实现高精度分类。
- 在低性能硬件上保持高准确率，优于传统彩色眼底摄影方法。
- 为资源匮乏地区提供可行的眼底病早期筛查方案。
深度学习已成为医学图像分类的主流方法。本文旨在将这些进展应用于超广角（UWF）眼底成像数据集。由于UWF图像能精准诊断多种视网膜疾病，准确分类并实现早期干预至关重要。然而，人工处理耗时耗力，自动化面临两大挑战：其一，高性能通常依赖高算力资源，而人工智能医疗技术更适用于医疗资源有限的场景，但在这些环境中使用高性能计算单元存在困难；其二，现有彩色眼底照相（CFP）方法存在精度不足的问题。一般而言，UWF方法提供的视网膜诊断信息比CFP更丰富，但多数研究仍基于CFP。因此，本文证明通过策略性数据增强和模型集成等方法，可在低性能设备上高效应对这些问题，兼顾性能与计算资源开销，充分挖掘UWF图像的价值。
原文摘要 · Abstract (English)
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.
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