卷积神经网络(CNN)是一种有前途的技术,用于基于乳房X线照片检测乳腺癌。从头开始训练CNN,然而,需要大量的标记数据。这样的要求对于诸如乳房摄影肿瘤图像的某些种类的医学图像数据通常是不可行的。由于改善CNN分类器的性能需要更多的训练数据,创造新的训练图像,图像增强,是解决这个问题的一个办法。我们应用了生成对抗网络(GAN)从数字数据库中生成合成乳房X线摄影图像,以筛查乳房X线摄影(DDSM)。从DDSM,我们从图像中裁剪出两组感兴趣区域(ROI):正常和异常(癌症/肿瘤)。那些ROI是用来训练GAN的,然后GAN生成合成图像。为了与仿射变换增强方法进行比较,比如旋转,shifting,缩放,等。,我们使用了六组ROI[三个简单组:仿射增强,GAN合成,真实(原始),和三个简单组中任何两个的三个混合组],用于从头开始训练CNN分类器。And,我们使用了训练中未使用的真实ROI来验证分类结果.我们的研究结果表明,为了对DDSM中的正常ROI和异常ROI进行分类,在训练数据中添加GAN生成的ROI可以帮助分类器防止过拟合,以及验证准确性,GAN的图像增强性能比仿射变换好3.6%。因此,GAN可能是一种理想的增强方法。通过GAN或仿射变换增强的图像不能替代真实图像来训练CNN分类器,因为训练集中缺少真实图像会导致过度拟合。
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, is one solution to this problem. 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. For comparison 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 any two 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 ROIs from DDSM, adding GAN-generated ROIs in the training data can help the classifier prevent overfitting, and on validation accuracy, the GAN performs about 3.6% better than affine transformations for image augmentation. Therefore, GAN could be an ideal augmentation approach. The images augmented by GAN or affine transformation cannot substitute for real images to train CNN classifiers because the absence of real images in the training set will cause over-fitting.