A deep feature based framework for breast masses classification
A deep feature based framework for breast masses classification
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DOI：
10.1016/j.neucom.2016.02.060
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发表时间：
2016-07-12
期刊：
影响因子：
6
通讯作者：
Li, Jie
中科院分区：
文献类型：
--
作者：
Jiao, Zhicheng;Gao, Xinbo;Li, Jie
肿块的特征性分型在乳腺癌的诊断中起着至关重要的作用。现有的计算机辅助诊断（Computer Aided Diagnosis，CAD）方法往往利用低层或中层特征，这些特征不能很好地模拟真实的诊断过程，给提高分类性能增加了困难。在本文中，我们设计了一个基于深度特征的框架，用于乳腺肿块分类任务。它主要包含卷积神经网络（CNN）和决策机制。结合训练好的CNN从原始图像中自动提取的强度信息和深度特征，我们提出的方法可以更好地模拟医生操作的诊断过程，并达到最先进的性能。在该框架中，医生对海量图像的整体印象和局部印象分别由高层和中层的深度特征表示。同时，原始图像被视为乳腺肿块的详细描述。然后，基于上述特征的分类器组合使用，以预测测试图像的类别。并对基于不同特征的分类器的分类结果进行联合分析，以确定测试图像的类型。在两种特征可视化方法的帮助下，从不同层次提取的深层特征在分类性能和诊断模拟方面都很有效。此外，我们的方法被应用到DDSM数据集，并取得了较高的精度下的两个客观评价措施。(C)© 2016 Elsevier B.V.版权所有。
Characteristic classification of mass plays a role of vital importance in diagnosis of breast cancer. The existing computer aided diagnosis (CAD) methods used to benefit a lot from low-level or middle-level features which are not that good at the simulation of real diagnostic processes, adding difficulties in improving the classification performance. In this paper, we design a deep feature based framework for breast mass classification task. It mainly contains a convolutional neural network (CNN) and a decision mechanism. Combining intensity information and deep features automatically extracted by the trained CNN from the original image, our proposed method could better simulate the diagnostic procedure operated by doctors and achieved state-of-art performance. In this framework, doctors' global and local impressions left by mass images were represented by deep features extracted from two different layers called high-level and middle-level features. Meanwhile, the original images were regarded as detailed descriptions of the breast mass. Then, classifiers based on features above were used in combination to predict classes of test images. And outcomes of classifiers based on different features were analyzed jointly to determine the types of test images. With the help of two kinds of feature visualization methods, deep features extracted from different layers illustrate effective in classification performance and diagnosis simulation. In addition, our method was applied to DDSM dataset and achieved high accuracy under two objective evaluation measures. (C) 2016 Elsevier B.V. All rights reserved.