Retinal disease is one of the primary causes of visual impairment, and early diagnosis is essential for preventing further deterioration. Nowadays, many works have explored Transformers for diagnosing diseases due to their strong visual representation capabilities. However, retinal diseases exhibit milder forms and often present with overlapping signs, which pose great difficulties for accurate multi-class classification. Therefore, we propose a new framework named Multi-Scale Patch Message Passing Swin Transformer for multi-class retinal disease classification. Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases. Moreover, considering the various scale of pathological features we integrate multiple PMP modules for different patch sizes. For evaluation, we have constructed a new dataset, named OPTOS dataset, consisting of 1,033 high-resolution fundus images photographed by Optos camera and conducted comprehensive experiments to validate the efficacy of our proposed method. And the results on both the public dataset and our dataset demonstrate that our method achieves remarkable performance compared to state-of-the-art methods.

翻译：视网膜疾病是导致视觉障碍的主要原因之一，早期诊断对于防止病情进一步恶化至关重要。近年来，许多工作探索了基于Transformer的疾病诊断方法，因其具备强大的视觉表征能力。然而，视网膜疾病症状通常较为轻微，且常伴有重叠体征，这给多类别精准分类带来了巨大困难。为此，我们提出了一种名为多尺度补丁消息传递Swin Transformer的新框架，用于多类别视网膜疾病分类。具体而言，我们基于消息传递机制设计了补丁消息传递模块，以建立病理语义特征的全局交互，并进一步挖掘不同疾病之间的细微差异。此外，考虑到病理特征的多尺度特性，我们针对不同补丁大小集成了多个PMP模块。为了进行评估，我们构建了一个名为OPTOS数据集的新数据集，包含由Optos相机拍摄的1,033张高分辨率眼底图像，并开展了全面实验以验证所提方法的有效性。在公共数据集和我们构建的数据集上的结果表明，与现有最先进方法相比，我们的方法取得了显著性能。