论文标题
基于分割的信息提取和融合的眼底图像中的青光眼检测
Segmentation-based Information Extraction and Amalgamation in Fundus Images for Glaucoma Detection
论文作者
论文摘要
青光眼是一种严重的盲目疾病，迫切需要自动检测方法来减轻眼科医生的稀缺性。许多作品已经提出采用深度学习方法，涉及对光盘和杯子进行分割进行青光眼检测，其中分割过程通常仅被视为上游子任务。在青光眼评估中，底底图像与分割面具之间的关系很少探索。我们为青光眼检测任务提出了一种基于分割的信息提取和融合方法，该方法利用了分割掩模的稳健性，而无需忽略原始底面图像中的丰富信息。私有数据集和公共数据集的实验结果表明，我们所提出的方法的表现优于所有仅利用底面图像或口罩的模型。
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.
