DETECTION OF AGE-RELATED MACULAR DEGENERATION VIA DEEP LEARNING

被引：51

作者：

Burlina, P. ^{[1
,2
,3
]}

Freund, D. E. ^{[1
]}

Joshi, N. ^{[1
]}

Wolfson, Y. ^{[3
]}

Bressler, N. M. ^{[3
]}

机构：

[1] Johns Hopkins Univ, Appl Phys Lab, Baltimore, MD 21218 USA

[2] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA

[3] Johns Hopkins Univ, Wilmer Eye Inst, Sch Med, Baltimore, MD 21218 USA

来源：

2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) | 2016年

关键词：

Age-related macular degeneration; deep learning; pre-trained networks; SUBGROUP ANALYSIS; RANIBIZUMAB; VERTEPORFIN;

D O I：

10.1109/ISBI.2016.7493240

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Age-related macular generation (AMD) - when left untreated - is the main cause of blindness for individuals over the age of 50. With the US population now counting over 100 million individuals over 50, it is imperative to develop methods that can effectively determine which individuals with an earlier, often asymptomatic stage, are at risk of developing the advanced stage that can cause severe vision loss. This paper studies the appropriateness of the transfer of image features computed from pre-trained deep neural networks to the problem in AMD detection. Tests using over 5600 images from the NIH AREDS dataset (the largest dataset used thus far for AMD image analysis studies) show good preliminary results (between nearly 92% and 95% accuracy).

引用

页码：184 / 188

页数：5

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