Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis
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DOI：
10.1016/j.eng.2020.04.012
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发表时间：
2021-10-22
期刊：
影响因子：
12.8
通讯作者：
Yao, Yu-Feng
中科院分区：
文献类型：
--
作者：
Xu, Yesheng;Kong, Ming;Yao, Yu-Feng
感染性角膜炎是最常见的角膜疾病，其中病原体在角膜中生长导致角膜组织的炎症和破坏。感染性角膜炎是一种医疗紧急情况，需要快速和准确的诊断，以确保及时和精确的治疗，以阻止疾病进展并限制角膜损伤的程度；否则，它可能会发展成威胁视力甚至威胁全球的状况。在本文中，我们提出了一种序列水平深度模型，通过临床图像的分类来有效地区分感染性角膜疾病。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，并理清感染性角膜炎临床图像分类的信息特征。在对比中，所提出的序列级深度模型的诊断准确率达到80%，远远优于421名眼科医生对120张测试图像的49.27% +/- 11.5%的诊断准确率。(c) 2021年作者。由爱思唯尔有限公司代中国工程院高等教育出版社有限公司出版。
Infectious keratitis is the most common condition of corneal diseases in which a 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 to ensure prompt and precise treatment to halt the disease progression and to limit the extent of corneal damage; otherwise, it may develop a sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential level deep model to effectively discriminate 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 a comparison, the performance of the proposed sequential-level deep model achieved 80% diagnostic accuracy, far better than the 49.27% +/- 11.5% diagnostic accuracy achieved by 421 ophthalmologists over 120 test images. (C) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.