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首页> 外文会议>International Workshop on Breast Imaging >Breast Cancer Detection Using Synthetic Mammograms from Generative Adversarial Networks in Convolutional Neural Networks
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Breast Cancer Detection Using Synthetic Mammograms from Generative Adversarial Networks in Convolutional Neural Networks

机译：乳腺癌检测在卷积神经网络中的生成对抗网络中的合成乳房X线照片

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The Convolutional Neural Network (CNN) is a promising technique to detect breast cancer based on mammograms. Training the CNN from scratch, however, requires a large amount of labeled data. Such a requirement usually is infeasible for some kinds of medical image data such as mammographic tumor images. Because improvement of the performance of a CNN classifier requires more training data, the creation of new training images-- image augmentation - could be one solution to this problem. In this study, we applied the Generative Adversarial Network (GAN) to generate synthetic mammographic images from the Digital Database for Screening Mammography (DDSM). From the DDSM, we cropped two sets of regions of interest (ROIs) from the images: normal and abnormal (cancer/tumor) Those ROIs were used to train the GAN, and the GAN then generated synthetic images. To compare the GAN with the affine transformation augmentation methods, such as rotation, shifting, scaling, etc., we used six groups of ROIs (three simple groups: affine augmented, GAN synthetic, real (original), and three mixture groups of each pair of the three simple groups) for each to train a CNN classifier from scratch. And, we used real ROIs that were not used in training to validate classification outcomes. Our results show that, to classify the normal ROIs and abnormal (tumor) ROIs from DDSM, adding GAN-generated ROIs to the training data can reduce overfitting of the classifier. But the affine transformations performed slightly better than GAN. Therefore, GAN could be an optional augmentation approach. The images augmented by GAN or affine transformation cannot substitute entirely for real images to train CNN classifiers because the absence of real images in the training set will cause serious over-fitting with more training.
机译：的卷积神经网络（CNN）是有前途的技术来检测基于乳房X线照片乳腺癌。从头训练CNN，但是，需要大量的标签数据。这样的要求通常是不可行的某些种的医用图像数据的诸如乳房X线照相肿瘤图像。由于CNN分类器的性能的提高，需要更多的训练数据，创造新的培训images--图像增强的 - 可能是一个解决这个问题。在这项研究中，我们采用了剖成对抗性网络（GAN），以从数字数据库合成X线影像的乳房摄影筛检（DDSM）。从DDSM，我们裁剪两套从图像中感兴趣（投资回报）​​区域：那些ROI被用来训练GAN正常和异常（癌症/肿瘤），然后将生成的GAN合成影像。为了比较与仿射变换的增强方法，如旋转，平移，缩放等的GAN，我们使用了六组ROI的（三个简单的组：仿射增强，GAN合成的，真实的（原始），每个三组混合物一对三个简单的基团）为每个从头训练CNN分类器。而且，我们使用了未在训练用于验证分类结果的真实投资回报率。我们的结果表明，从DDSM正常的ROI和异常（肿瘤）的ROI进行分类，加入GAN-生成的ROI训练数据可以减少分类器的过度拟合。但仿射变换比甘表现稍好。因此，GAN可能是一个可选的增强方法。由GaN或仿射变换增强图像不能完全取代真实的图像训练CNN分类，因为在训练组没有真正的图像会造成过拟合有更多的培训严重。

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