Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data
Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data
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DOI：
10.1016/j.compmedimag.2016.07.004
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发表时间：
2017-04-01
影响因子：
5.7
通讯作者：
Qian, Wei
中科院分区：
文献类型：
--
作者：
Sun, Wenqing;Tseng, Tzu-Liang (Bill);Qian, Wei
在这项研究中，我们开发了一种基于图的半监督学习（SSL）方案，使用深度卷积神经网络（CNN）进行乳腺癌诊断。CNN通常需要大量的标记数据来训练和微调参数，而我们提出的方案只需要训练集中的一小部分标记数据。该诊断系统包括数据加权、特征选择、划分协同训练数据标记和CNN四个模块。本研究使用了3158个感兴趣区域（ROI），每个ROI包含从1874对乳房X线照片中提取的肿块。其中100个ROI被视为标记数据，其余被视为未标记数据。在我们的研究中观察到的曲线下面积（AUC）为0.8818，使用混合标记和未标记数据时CNN的准确度为0.8243。(C)2016由Elsevier Ltd.出版
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. (C) 2016 Published by Elsevier Ltd.