[1]蒋杰伟,雷舒陶,耿苗苗,等.融合可解释性特征的糖尿病视网膜病变自动诊断[J].中国医学物理学杂志,2022,39(5):640-646.[doi:DOI:10.3969/j.issn.1005-202X.2022.05.020]
　JIANG Jiewei,LEI Shutao,GENG Miaomiao,et al.Automatic diagnosis of diabetic retinopathy based on interpretable features fusion[J].Chinese Journal of Medical Physics,2022,39(5):640-646.[doi:DOI:10.3969/j.issn.1005-202X.2022.05.020]
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融合可解释性特征的糖尿病视网膜病变自动诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第5期
页码:
640-646
栏目:
医学人工智能
出版日期:
2022-05-27

文章信息/Info

Title:
Automatic diagnosis of diabetic retinopathy based on interpretable features fusion
文章编号:
1005-202X（2022）05-0640-07
作者:
蒋杰伟1; 雷舒陶2; 耿苗苗1; 巩稼民1; 2; 朱泽昊2; 张运生1; 刘芳2; 吴艺杰2; 王育文3; 李中文3
1.西安邮电大学电子工程学院， 陕西 西安 710121； 2.西安邮电大学通信与信息工程学院， 陕西 西安 710121； 3.温州医科大学宁波市眼科医院， 浙江 宁波 315000
Author(s):
JIANG Jiewei1;  LEI Shutao2; GENG Miaomiao1;  GONG Jiamin1; 2;  ZHU Zehao2;  ZHANG Yunsheng1;  LIU Fang2;  WU Yijie2;  WANG Yuwen3;  LI Zhongwen3
1. School of Electronic Engineering, Xian University of Posts & Telecommunications, Xian 710121, China 2. School of Communication and Information Engineering, Xian University of Posts & Telecommunications, Xian 710121, China 3. Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
关键词:
糖尿病视网膜病变; 深度学习; 集成学习; 可解释性模型; 支持向量机
Keywords:
Keywords: diabetic retinopathy deep learning ensemble learning interpretable model support vector machine
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2022.05.020
文献标志码:
A
摘要:
糖尿病视网膜病变（DR）已成为全球4大主要致盲疾病之一，及早确诊可以有效降低患者视力受损的风险。通过融合深度学习可解释性特征，提出一种DR自动诊断方法，首先利用导向梯度加权类激活映射图和显著图两种可解释性方法生成不同标记的病灶图像，再通过卷积神经网络提取原图像和两种生成图像的特征向量，最后融合3种特征向量并输入到支持向量机中以实现DR的自动诊断。在1 443张彩色眼底图像构成的数据集上，相对于基础ResNet50模型，该方法诊断准确率提高3.6%，特异性提高2.4%，灵敏度提高5.8%，精度提高4.6%，Kappa系数提高7.9%，实验结果表明该方法能有效降低误诊的风险。
Abstract:
Abstract: Diabetic retinopathy (DR) has become one of the 4 major common causes of blindness, and the early diagnosis can effectively reduce the risk of visual impairment. By the fusion of the interpretable features of deep learning, an automatic diagnosis method of DR is proposed in the study. After different labeled lesion images are generated by two interpretable techniques, namely guided gradient-weighted class activation mapping and saliency map, the feature vectors of the original image and the two generated images are extracted via convolutional neural networks. Finally, the fusion of 3 kinds of feature vectors is carried out, and the results are input into support vector machine for realizing the automatic diagnosis of DR. For the data set with 1 443 color fundus images, compared with those of basic ResNet50 model, the accuracy, specificity, sensitivity, precision and Kappa coefficient of the proposed method are improved by 3.6%, 2.4%, 5.8%, 4.6% and 7.9%, respectively. The experimental results show that the proposed method can effectively reduce the risk of DR misdiagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2021-11-19 【基金项目】国家自然科学基金（61775180）；国家重点研发计划（2018YFC0116500）；陕西省自然科学基础研究计划（2022JM-380）；宁波市科技计划（2019C50045）；高校青年教师科研基金（205020022） 【作者简介】蒋杰伟，博士，讲师，研究方向：深度学习、机器学习和医疗图像处理，E-mail: jiangjw924@126.com
更新日期/Last Update: 2022-05-27