Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

doi:10.1109/EMBC.2018.8512828

	Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
	Kang Zhou1 ; Zaiwang Gu 2; Wen Liu1 ; Weixin Luo2 ; Jun Cheng 2; Shenghua Gao1 ; Jiang Liu 2
	2018-07
会议录名称	2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
ISSN	1557-170X
卷号	2018-July
页码	2724-2727
发表状态	已发表
DOI	10.1109/EMBC.2018.8512828
摘要	Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for the DR screening is in high demand. Considering the resolution of retinal image is very high, where small pathological tissues can be detected only with large resolution image and large local receptive field are required to identify those late stage disease, but directly training a neural network with very deep architecture and high resolution image is both time computational expensive and difficult because of gradient vanishing/exploding problem, we propose a Multi-Cell architecture which gradually increases the depth of deep neural network and the resolution of input image, which both boosts the training time but also improves the classification accuracy. Further, considering the different stages of DR actually progress gradually, which means the labels of different stages are related. To considering the relationships of images with different stages, we propose a Multi-Task learning strategy which predicts the label with both classification and regression. Experimental results on the Kaggle dataset show that our method achieves a Kappa of 0.841 on test set which is the 4th rank of all state-of-the-arts methods. Further, our Multi-Cell Multi-Task Convolutional Neural Networks (M2CNN) solution is a general framework, which can be readily integrated with many other deep neural network architectures.
关键词	Image resolution Computer architecture Diseases Training Diabetes Retina Microprocessors
会议地点	Honolulu, HI
会议日期	18-21 July 2018
URL	查看原文
收录类别	EI
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20184906171839
原始文献类型	Conferences
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/28210
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_高盛华组信息科学与技术学院_博士生
作者单位	1.School of Information Science and Technology, ShanghaiTech University, China 2.Ningbo Institute of Materials Technology and Engineering, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Kang Zhou,Zaiwang Gu,Wen Liu,et al. Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading[C]:Institute of Electrical and Electronics Engineers Inc.,2018:2724-2727.