Machine learning has significantly advanced healthcare by aiding in disease prevention and treatment identification. However, accessing patient data can be challenging due to privacy concerns and strict regulations. Generating synthetic, realistic data offers a potential solution for overcoming these limitations, and recent studies suggest that fine-tuning foundation models can produce such data effectively. In this study, we explore the potential of foundation models for generating realistic medical images, particularly chest x-rays, and assess how their performance improves with fine-tuning. We propose using a Latent Diffusion Model, starting with a pre-trained foundation model and refining it through various configurations. Additionally, we performed experiments with input from a medical professional to assess the realism of the images produced by each trained model.

本研究解决了因隐私和法规限制而获取患者数据困难的问题，提出通过基础模型生成合成医学影像的创新方法。通过应用潜在扩散模型，研究表明微调基础模型能够有效提高生成胸部X光片的质量与真实性，可能为医学影像生成领域带来重要影响。

探索基础模型在合成医学影像中的应用：针对胸部X光片的研究及微调技术