In this paper, we explore the feasibility of using generative models, specifically Progressive Growing GANs (PG-GANs) and Stable Diffusion fine-tuning, to generate synthetic chest X-ray images for medical diagnosis purposes. Due to ethical concerns, obtaining sufficient medical data for machine learning is a challenge, which our approach aims to address by synthesising more data. We utilised the Chest X-ray 14 dataset for our experiments and evaluated the performance of our models through qualitative and quantitative analysis. Our results show that the generated images are visually convincing and can be used to improve the accuracy of classification models. However, further work is needed to address issues such as overfitting and the limited availability of real data for training and testing. The potential of our approach to contribute to more effective medical diagnosis through deep learning is promising, and we believe that continued advancements in image generation technology will lead to even more promising results in the future.

翻译：本文探讨了使用生成模型（具体为渐进式增长生成对抗网络（PG-GANs）和稳定扩散微调）生成合成胸部X光图像用于医学诊断的可行性。由于伦理问题，获取足够的医学数据用于机器学习面临挑战，而我们的方法旨在通过合成更多数据来解决这一问题。我们利用ChestX-ray14数据集进行实验，并通过定性和定量分析评估模型性能。结果表明，生成的图像在视觉上具有说服力，并能用于提升分类模型的准确性。然而，仍需进一步研究以解决过拟合及训练和测试中真实数据有限等问题。我们的方法有望通过深度学习促进更有效的医学诊断，我们相信图像生成技术的持续进步将在未来带来更令人期待的结果。