PMP-Swin: Multi-Scale Patch Message Passing Swin Transformer for Retinal
  Disease Classification
Zhihan Yang Zhiming Cheng Tengjin Weng Shucheng He Yaqi Wang Xin Ye Shuai Wang
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.