Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques

• URL: http://arxiv.org/abs/2409.04424v1
• Date: Fri, 6 Sep 2024 17:36:08 GMT
• Title: Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
• Authors: Davide Clode da Silva, Marina Musse Bernardes, Nathalia Giacomini Ceretta, Gabriel Vaz de Souza, Gabriel Fonseca Silva, Rafael Heitor Bordini, Soraia Raupp Musse,
• Abstract summary: 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. Recent studies suggest that fine-tuning foundation models can produce such data effectively.
• Score: 0.49000940389224884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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