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Specifically, we introduce a cross-head relative position encoding scheme into a standard self-attention module to capture contextual information among different regions and employ a token-attention feed-forward network to place greater focus on valuable abnormal regions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 2723 images from CED patients are used to train our system. It achieves an accuracy of 89.53%, and the area under the receiver operating characteristic curve (AUC) is 0.958 (95% CI 0.943\u20130.971) on images from multiple centres.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our system is the first artificial intelligence-based system for diagnosing CEDs worldwide. 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