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Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
Engineering ( IF 12.8 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.eng.2020.04.012
Yesheng Xu , Ming Kong , Wenjia Xie , Runping Duan , Zhengqing Fang , Yuxiao Lin , Qiang Zhu , Siliang Tang , Fei Wu , Yu-Feng Yao

Infectious keratitis is the most common entities of corneal diseases, in which pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency, for which a rapid and accurate diagnosis is needed for speedy initiation of prompt and precise treatment to halt the disease progress and to limit the extent of corneal damage; otherwise it may develop sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential-level deep learning model to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In competition with 421 ophthalmologists, the performance of the proposed sequential-level deep model achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic accuracy achieved by ophthalmologists over 120 test images.

中文翻译:

感染性角膜炎临床图像分类中的深度序列特征学习

传染性角膜炎是角膜疾病中最常见的实体,其中病原体在角膜中生长,导致角膜组织发炎和破坏。感染性角膜炎是一种医疗急症,需要快速准确的诊断,以便迅速启动及时准确的治疗,以阻止疾病进展并限制角膜损伤的程度;否则它可能会发展为威胁视力甚至威胁眼球的疾病。在本文中,我们提出了一种序列级深度学习模型,通过临床图像的分类来有效区分传染性角膜疾病的区别和微妙之处。在这种方法中,我们设计了一种适当的机制来保留临床图像的空间结构并解开感染性角膜炎临床图像分类的信息特征。在与 421 名眼科医生的竞争中,所提出的序列级深度模型的性能达到了 80.00% 的诊断准确率,远高于眼科医生在 120 幅测试图像上达到的 49.27% 的诊断准确率。
更新日期:2020-07-01
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