PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal Disease Classification
This work addresses early diagnosis of retinal diseases to prevent visual impairment, but it is incremental as it builds on existing Transformer methods with a novel module.
The paper tackled the problem of accurate multi-class retinal disease classification by proposing a Multi-Scale Patch Message Passing Swin Transformer, which achieved remarkable performance compared to state-of-the-art methods on both public and new datasets.
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.