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《工程(英文)》 >> 2021年 第7卷 第7期 doi: 10.1016/j.eng.2020.04.012

基于深度序列特征学习的临床感染性角膜炎图像分类

a Department of Ophthalmology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China
b College of Computer Science and Technology, Zhejiang University, Hangzhou 31002, China

# These authors contributed equally to this work.

收稿日期: 2020-01-17 修回日期: 2020-03-18 录用日期: 2020-04-20 发布日期: 2020-07-15

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摘要

感染性角膜炎是最常见的角膜疾病之一,病原体在角膜中生长引发炎症反应并损伤角膜组织。感染性角膜炎作为一种临床急症,需要快速、精准的诊断,确保患者能够得到及时、准确的治疗,从而遏制疾病的发展,并将其对角膜的损伤降到最低。否则患者会有失明的风险,严重者甚至会失去眼球。本文提出了一种深度序列特征学习模型,该模型能够通过对临床图像的分类高效地鉴别不同的感染性角膜炎。我们针对感染性角膜炎的特点设计了一种能够解耦临床图像中最具区别
性的特征并保持其空间结构的机制。通过比较,我们提出的深度序列特征学习模型在120张图像的测试集上的准确率能够达到80%,远高于421位眼科医生所能达到的平均水平[(49.27 ± 11.5)]%。

 

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