Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases

被引：24
|
作者：
Rampasek, Ladislav [1 ,2 ,3 ]
Goldenberg, Anna [1 ,2 ,3 ]
机构：
[1] Univ Toronto, Dept Comp Sci, Toronto, ON, Canada
[2] Hosp Sick Children, Toronto, ON, Canada
[3] Vector Inst, Toronto, ON, Canada
来源：
CELL | 2018年 / 172卷 / 05期
关键词：
D O I：
10.1016/j.cell.2018.02.013
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
Kermany et al. report an application of a neural network trained on millions of everyday images to a database of thousands of retinal tomography images that they gathered and expert labeled, resulting in a rapid and accurate diagnosis of retinal diseases.
引用
收藏
页码：893 / 895
页数：4
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