Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains

Pierre Joseph Marcel Chambon, Christian Bluethgen, Curtis Langlotz, Akshay Chaudhari

Keywords: Foundational model, multi-modal, stable diffusion, domain adaptation, fine-tuning, medical imaging, radiology

TL;DR: Text conditioned generative models can be fine-tuned to generate clinically accurate images, capable of inserting abnormalities and creating high-quality synthetic data.

Abstract: Multi-modal foundational models are trained on millions of pairs of natural images and texts, frequently obtained through web-crawling approaches. Although their performance is excellent, these models do not generalize well to other domains, such as medical imaging, especially when these domains do not resemble the centric-like images that can be found on the web. In this study, we assess the ability of the stable diffusion model to generate domain-specific images in the particular case of medical imaging. Based on quantitative and qualitative evaluations of the main components of the stable diffusion pipeline (the variational autoencoder, the U-Net and the text-encoder), we explore several approaches to fine-tune stable diffusion to generate radiological images, which accurately represent the clinical content of conditional text prompts. Our best-performing model improves upon the stable diffusion baseline and can be correctly conditioned to insert an abnormality on a synthetic radiology image.