用分割图引导双分支网络，提升眼底病诊断准确率
SegImgNet: Segmentation-Guided Dual-Branch Network for Retinal Disease Diagnoses
- 双分支结构分别处理眼底图像和分割图，独立提取特征
- 在AIROGS和e-ROP数据集上均优于现有方法
- 引入分割引导注意力块，增强关键结构特征捕捉
眼底图像在疾病诊断中至关重要，因其提供关键的解剖结构信息。然而，如何有效捕捉结构特征并融合上下文信息仍是挑战。本文提出分割引导的双分支网络SegImgNet，利用眼底图像及其分割图进行疾病诊断。该网络包含一个分割模块，生成多尺度的结构特征图；分类模块采用两个编码器，分别从分割图像和原始眼底图像中独立提取特征。为进一步提升特征表达，引入分割引导注意力（SGA）模块，利用分割模块的特征图优化分类过程。在公开的AIROGS数据集和私有的e-ROP数据集上进行评估，实验结果表明SegImgNet持续优于现有方法，验证了其在眼底病诊断中的有效性。代码已开源。
原文摘要 · Abstract (English)
Retinal image plays a crucial role in diagnosing various diseases, as retinal structures provide essential diagnostic information. However, effectively capturing structural features while integrating them with contextual information from retinal images remains a challenge. In this work, we propose segmentation-guided dual-branch network for retinal disease diagnosis using retinal images and their segmentation maps, named SegImgNet. SegImgNet incorporates a segmentation module to generate multi-scale retinal structural feature maps from retinal images. The classification module employs two encoders to independently extract features from segmented images and retinal images for disease classification. To further enhance feature extraction, we introduce the Segmentation-Guided Attention (SGA) block, which leverages feature maps from the segmentation module to refine the classification process. We evaluate SegImgNet on the public AIROGS dataset and the private e-ROP dataset. Experimental results demonstrate that SegImgNet consistently outperforms existing methods, underscoring its effectiveness in retinal disease diagnosis. The code is publicly available at https://github.com/hawk-sudo/SegImgNet.
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