Glaucoma is a severe blinding disease, for which automatic detection methods are urgently needed to alleviate the scarcity of ophthalmologists. Many works have proposed to employ deep learning methods that involve the segmentation of optic disc and cup for glaucoma detection, in which the segmentation process is often considered merely as an upstream sub-task. The relationship between fundus images and segmentation masks in terms of joint decision-making in glaucoma assessment is rarely explored. We propose a novel segmentation-based information extraction and amalgamation method for the task of glaucoma detection, which leverages the robustness of segmentation masks without disregarding the rich information in the original fundus images. Experimental results on both private and public datasets demonstrate that our proposed method outperforms all models that utilize solely either fundus images or masks.
翻译:青光眼是一种严重的致盲疾病,迫切需要用自动检测方法来缓解眼科医生的短缺,许多工作提议采用深层学习方法,包括光碟和玻璃分离以探测青光眼,其中分解过程往往仅被视为上游子任务。在光谱评估中,基流图像和隔热面罩在联合决策方面的关系很少得到探讨。我们提出一种新的基于分化的信息提取和合并方法,用于光谱检测,利用分解面罩的稳健性,而不忽视原始基流图像中的丰富信息。私人和公共数据集的实验结果显示,我们拟议的方法超越了仅使用基流图像或面罩的所有模型。