Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
This work addresses data scarcity in medical imaging for researchers and clinicians, but it is incremental as it applies existing fine-tuning techniques to a specific domain.
The study tackled the challenge of generating realistic synthetic chest x-ray images to address privacy and regulatory issues in healthcare data access by fine-tuning a Latent Diffusion Model, resulting in improved performance as assessed by a medical professional for realism.
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.