Purpose

In recent years, convolutional neural network (CNN), an artificial intelligence technology with superior image recognition, has become increasingly popular and frequently used for classification tasks in medical imaging. However, the amount of labelled data available for classifying medical images is often significantly less than that of natural images, and the handling of rare diseases is often challenging. To overcome these problems, data augmentation has been performed using generative adversarial networks (GANs). However, conventional GAN cannot effectively handle the various shapes of tumours because it randomly generates images. In this study, we introduced semi-conditional InfoGAN, which enables some labels to be added to InfoGAN, for the generation of shape-controlled tumour images. InfoGAN is a derived model of GAN, and it can represent object features in images without any label.

Methods

Chest computed tomography images of 66 patients diagnosed with three histological types of lung cancer (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer) were used for analysis. To investigate the applicability of the generated images, we classified the histological types of lung cancer using a CNN that was pre-trained with the generated images.

Results

As a result of the training, InfoGAN was possible to generate images that controlled the diameters of each lesion and the presence or absence of the chest wall. The classification accuracy of the pre-trained CNN was 57.7%, which was higher than that of the CNN trained only with real images (34.2%), thereby suggesting the potential of image generation.

Conclusion

The applicability of semi-conditional InfoGAN for feature learning and representation in medical images was demonstrated in this study. InfoGAN can perform constant feature learning and generate images with a variety of shapes using a small dataset.

中文翻译：

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半条件InfoGAN合成形状控制肺癌的CT图像及其在类型分类中的适用性

目的

近年来，具有卓越图像识别能力的人工智能技术卷积神经网络（CNN）变得越来越流行，并经常用于医学成像中的分类任务。然而，可用于对医学图像进行分类的标记数据量通常大大少于自然图像，并且罕见疾病的处理通常具有挑战性。为了克服这些问题，已经使用生成对抗网络（GAN）进行了数据增强。然而，常规GAN不能有效地处理各种形状的肿瘤，因为它随机地产生图像。在这项研究中，我们介绍了半条件InfoGAN，它可以将一些标签添加到InfoGAN中，以生成形状控制的肿瘤图像。InfoGAN是GAN的衍生模型，

方法

使用66例诊断为三种组织学类型的肺癌（腺癌，鳞状细胞癌和小细胞肺癌）的患者的胸部计算机断层扫描图像进行分析。为了调查所生成图像的适用性，我们使用了经过预生成图像训练的CNN对肺癌的组织学类型进行了分类。

结果

训练的结果是，InfoGAN可以生成控制每个病变直径以及胸壁有无的图像。预训练的CNN的分类精度为57.7％，高于仅使用真实图像训练的CNN的分类精度（34.2％），从而暗示了图像生成的潜力。

结论

这项研究证明了半条件InfoGAN在医学图像中进行特征学习和表示的适用性。InfoGAN可以执行恒定特征学习，并使用小型数据集生成具有各种形状的图像。