Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, both varieties of the mode collapse problem are investigated, and their subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization with the Deep Convolutional GAN and Auxiliary Classifier GAN to alleviate the mode collapse problems. Synthetically generated images are utilized for data augmentation and training a Vision Transformer model. The classification performance of the model is evaluated using accuracy, recall, and precision scores. Results demonstrate that the DCGAN and the ACGAN with adaptive input-image normalization outperform the DCGAN and ACGAN with un-normalized X-ray images as evidenced by the superior diversity scores and classification scores.

翻译：生物医学图像数据集可能因目标疾病的罕见性而存在不平衡问题。生成对抗网络通过生成合成图像以增强数据集，在解决这种不平衡中发挥关键作用。生成包含多样化特征的合成图像以准确反映训练图像中特征分布至关重要。此外，合成图像中缺乏多样化特征会降低机器学习分类器的性能。模式崩溃问题影响生成对抗网络生成多样化图像的能力。模式崩溃分为两类：类内模式崩溃和类间模式崩溃。本文研究了这两种模式崩溃问题，并评估了它们对合成X射线图像多样性的后续影响。本研究通过实验证明，将自适应输入图像归一化与深度卷积生成对抗网络和辅助分类器生成对抗网络相结合，能够有效缓解模式崩溃问题。合成生成的图像被用于数据增强和训练视觉变换器模型。使用准确率、召回率和精确率分数评估模型的分类性能。结果表明，采用自适应输入图像归一化的DCGAN和ACGAN在多样化分数和分类分数方面均优于使用未归一化X射线图像的DCGAN和ACGAN。