Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the trajectory of RD model development. The leading-edge performance of ViT-based models in RD can be largely credited to their scalability-their ability to improve as more parameters are added. As a result, ViT-based models tend to outshine traditional CNNs in RD applications, albeit at the cost of increased data and computational demands. ViTs also differ from CNNs in their approach to processing images, working with patches rather than local regions, which can complicate the precise localization of small, variably presented lesions in RD. In our study, we revisited and updated the architecture of a CNN model, specifically MobileNet, to enhance its utility in RD diagnostics. We found that an optimized MobileNet, through selective modifications, can surpass ViT-based models in various RD benchmarks, including diabetic retinopathy grading, detection of multiple fundus diseases, and classification of diabetic macular edema. The code is available at https://github.com/Retinal-Research/NN-MOBILENET

翻译：在过去几十年中，卷积神经网络（CNN）一直处于各类视网膜疾病（RD）检测与跟踪的前沿。尽管取得了成功，但2020年代视觉Transformer（ViT）的出现改变了RD模型发展的轨迹。基于ViT的模型在RD中的领先性能在很大程度上归功于其可扩展性——即随着参数增加而性能提升的能力。因此，基于ViT的模型在RD应用中往往优于传统CNN，尽管这以增加数据和计算需求为代价。ViT在图像处理方式上也与CNN不同，它通过图像块而非局部区域进行处理，这可能会使RD中微小且形态多变的病变的精确定位复杂化。在本研究中，我们重新审视并更新了CNN模型（具体为MobileNet）的架构，以增强其在RD诊断中的实用性。我们发现，通过选择性修改，优化后的MobileNet能够在各种RD基准测试中超越基于ViT的模型，包括糖尿病视网膜病变分级、多种眼底疾病检测以及糖尿病黄斑水肿分类。代码可在https://github.com/Retinal-Research/NN-MOBILENET获取。