论文标题
在可解释的机器学习的小儿眼底图像中自动检测乳头毛瘤
Automating Detection of Papilledema in Pediatric Fundus Images with Explainable Machine Learning
论文作者
论文摘要
乳头乳头瘤菌是一种眼科神经系统疾病，其中增加的颅内压会导致视神经肿胀。儿童中未诊断的乳头毛虫可能导致失明，可能是威胁生命的疾病（例如脑肿瘤）的标志。使用深度学习对眼底图像进行自动分析，可以促进对该综合征的稳健临床诊断，尤其是在存在伪造症带来的挑战的情况下，具有相似的眼睛外观，但具有明显的临床意义。我们提出了一种基于学习的深度算法，用于自动检测小儿乳头毛瘤。我们的方法基于通过数据扩展来检测可解释的乳头乳头指标的光盘定位和检测。现实世界中临床数据的实验表明，我们提出的方法具有与专家眼科医生相当的诊断准确性有效。
Papilledema is an ophthalmic neurologic disorder in which increased intracranial pressure leads to swelling of the optic nerves. Undiagnosed papilledema in children may lead to blindness and may be a sign of life-threatening conditions, such as brain tumors. Robust and accurate clinical diagnosis of this syndrome can be facilitated by automated analysis of fundus images using deep learning, especially in the presence of challenges posed by pseudopapilledema that has similar fundus appearance but distinct clinical implications. We present a deep learning-based algorithm for the automatic detection of pediatric papilledema. Our approach is based on optic disc localization and detection of explainable papilledema indicators through data augmentation. Experiments on real-world clinical data demonstrate that our proposed method is effective with a diagnostic accuracy comparable to expert ophthalmologists.
