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Invest Ophthalmol Vis Sci:深度学习的神经网络算法检测视网膜病变!

2018-01-31 cuiguizhong MedSci原创

美国加利福尼亚州圣何塞市圣克拉拉谷医疗中心眼科和斯坦福大学生物医学数据科学系的Lam C近日在Invest Ophthalmol Vis Sci发表了一项重要的工作,他们开发了一种自动化的方法来定位和辨别视网膜图像中的多种类型的特征。这套方法使用有限的数据进行训练,中间没有编码特征提取的算法。因为有的疾病的数据有限,所以可以将此算法推广到少数疾病检测的步骤中。

美国加利福尼亚州圣何塞市圣克拉拉谷医疗中心眼科和斯坦福大学生物医学数据科学系的Lam C近日在Invest Ophthalmol Vis Sci发表了一项重要的工作,他们开发了一种自动化的方法来定位和辨别视网膜图像中的多种类型的特征。这套方法使用有限的数据进行训练,中间没有编码特征提取的算法。因为有的疾病的数据有限,所以可以将此算法推广到少数疾病检测的步骤中。

两名眼科医生诊断了243幅视网膜图像,标记图像的重要部分,产生了1324个图像块,得到包含出血、微动脉瘤、渗出物、视网膜新血管形成或来自Kaggle数据集的正常结构等。用这些图像块来训练一个标准的卷积神经网络,达到可以预测上述五个类别。

他们使用该方法在eOphta数据库上验证了148个全视网膜微动脉瘤图像和47个分泌物。在0.94和0.95的ROC曲线下面积的像素分类得到微动脉瘤和分泌物,在0.86和0.64的精确回忆曲线下得到病灶面积。

因此,他们认为,区域训练的卷积神经网络可以产生病变特异性的概率图,能够检测和区分微妙的病理损伤,而这样的预测结果只需要每个损伤几百个训练样本就可以达到。

原文出处:

Lam, C., et al., Retinal Lesion Detection With Deep Learning Using Image Patches. Invest Ophthalmol Vis Sci, 2018. 59(1): p. 590-596.

本文系梅斯医学(MedSci)原创编译整理,转载需授权!

 

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    2018-11-14 智慧医人
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    2018-03-17 139****0239

    henhao

    0

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    2018-02-02 圣艮山

    学习了不少事情!!

    0

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  8. 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    2018-02-02 zutt
  9. 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    2018-02-02 txqjm

    谢谢了.学习

    0

  10. 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    2018-02-01 Y—xianghai

    学习了新知识

    0

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土耳其泰基尔达NamkKemal大学医学院眼科的Horozoglu F 近日在Curr Eye Res发表了他们的一项工作,他们探讨了幽门螺杆菌(H. pylori)感染对无中心性浆液脉络膜视网膜病变(CSC)患者的中央凹下脉络膜厚度(SFCT)的影响。

Nature:DHA这次闯祸了!科学家发现DHA被异常降解的产物是导致糖尿病视网膜病变的又一罪魁祸首

提到DHA,想必很多经常看电视的人以及有宝宝的人都很熟悉了。毕竟铺天盖地的奶粉广告中,提的最多,标注最明显的三个字母就是DHA。好像没加DHA都不好意思说自己卖的是奶粉一样,一定要把DHA标志做的够大,够显眼。

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