Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnostics 阅读
2016-08-11 11:16
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文章链接: http://arxiv.org/pdf/1608.02989v1.pdf
文章创新在于，使用CNN来做基于显微观察下的病情诊断。
文章实验针对3种疾病（疟疾、肺结核、肠道寄生虫卵）展开：
malaria in thick blood smears
tuberculosis in sputum samples
intestinal parasite eggs in stool samples
文章还介绍了为了固定手机而设计的器件
文章中介绍的实验的实物(居然用了一台三星手机)
文章采用的网络结果很简单，使用了4层隐藏层
Convolution layer: 7 filters of size 3 × 3.
Pooling layer: max-pooling, factor 2.
Convolution layer: 12 filters of size 2 × 2.
实验方法没什么特别的，不过结果好到爆炸！
文章创新在于，使用CNN来做基于显微观察下的病情诊断。
文章实验针对3种疾病（疟疾、肺结核、肠道寄生虫卵）展开：
malaria in thick blood smears
tuberculosis in sputum samples
intestinal parasite eggs in stool samples
文章还介绍了为了固定手机而设计的器件
文章中介绍的实验的实物(居然用了一台三星手机)
文章采用的网络结果很简单，使用了4层隐藏层
Convolution layer: 7 filters of size 3 × 3.
Pooling layer: max-pooling, factor 2.
Convolution layer: 12 filters of size 2 × 2.
实验方法没什么特别的，不过结果好到爆炸！
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