Mining Faces from Biomedical Literature using Deep Learning
Mining Faces from Biomedical Literature using Deep Learning
复制标题
使用深度学习从生物医学文献中挖掘面孔
DOI：
10.1145/3107411.3107476
复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Dawson M
中科院分区：
文献类型：
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作者：
Dawson M
获取大量标记的相关图像集对于生物医学成像算法的开发和测试至关重要。使用生物医学研究文章中的图像将有助于解决这个问题。然而，这种方法关键取决于能否从非常大的潜在有用图形集中识别出最相关的图像。本文仅使用合成数据训练深度卷积神经网络(CNN)分类器，以快速准确地标记来自生物医学文章的原始图像。我们将该方法应用于生物医学图像中的人脸检测，并表明该分类器能够从PubMed数据库中保存的31,000多张图像中检索包含人脸的图形，平均准确率为94.8%。然后，通过一个案例研究，通过帮助从目标文章中挖掘罕见遗传疾病患者的照片，证明了该分类器的实用性。这种方法很容易适应于促进其他类别的生物医学图像的检索。
Gaining access to large, labelled sets of relevant images is crucial for the development and testing of biomedical imaging algorithms. Using images found in biomedical research articles would contribute some way towards a solution to this problem. However, this approach critically depends on being able to identify the most relevant images from very large sets of potentially useful figures. In this paper a deep convolutional neural network (CNN) classifier is trained using only synthetic data, to rapidly and accurately label raw images taken from biomedical articles. We apply this method in the context of detecting faces in biomedical images; and show that the classifier is able to retrieve figures containing faces with an average precision of 94.8%, from a dataset of over 31,000 images taken from articles held in the PubMed database. The utility of the classifier is then demonstrated through a case study, by aiding the mining of photographs of patients with rare genetic disorders from targeted articles. This approach is readily adaptable to facilitate the retrieval of other categories of biomedical images.
作者回应：来自普通照片的诊断相关面部格式塔信息
DOI：
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发表时间：
2014
期刊：
影响因子：
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作者：
Q. Ferry;J. Steinberg;C. Webber;D. FitzPatrick;C. Ponting;Andrew Zisserman;C. Nellåker
通讯作者：
C. Nellåker