论文标题
评估深层卷积生成对抗网络，用于数据增强胸部X射线图像
Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images
论文作者
论文摘要
由于获得数据和耗时的注释的高成本，医疗图像数据集通常会失衡。在此类数据集上培训深层神经网络模型以准确地对医疗状况进行分类不会产生预期的结果，并且通常会超越多数类样本的数据。为了解决这个问题，通常通过位置增强技术（例如缩放，裁剪，翻转，填充，填充，旋转，翻译，仿射转换以及诸如亮度，对比度，饱和度和色调）以增加数据集尺寸的训练数据进行数据增强。这些增强技术不能保证在有限的数据（尤其是医疗图像数据）的域中具有优势，并且可能导致进一步的过度拟合。在这项工作中，我们通过生成建模（深度卷积生成的对抗网络）对胸部X射线数据集进行了数据增强，该模型创建了人工实例，该实例保留了与原始数据相似的特征和对模型的评估，从而导致了Inception Intecte（FID）分数（FID）分数为1.289。
Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations. Training deep neural network models on such datasets to accurately classify the medical condition does not yield desired results and often over-fits the data on majority class samples. In order to address this issue, data augmentation is often performed on training data by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. These augmentation techniques are not guaranteed to be advantageous in domains with limited data, especially medical image data, and could lead to further overfitting. In this work, we performed data augmentation on the Chest X-rays dataset through generative modeling (deep convolutional generative adversarial network) which creates artificial instances retaining similar characteristics to the original data and evaluation of the model resulted in Fréchet Distance of Inception (FID) score of 1.289.
