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Multi-modal foundation models are typically trained on millions of pairs ofnatural images and text captions, frequently obtained through web-crawlingapproaches. Although such models depict excellent generative capabilities, theydo not typically generalize well to specific domains such as medical imagesthat have fundamentally shifted distributions compared to natural images.Building generative models for medical images that faithfully depict clinicalcontext may help alleviate the paucity of healthcare datasets. Thus, in thisstudy, we seek to research and expand the representational capabilities oflarge pretrained foundation models to medical concepts, specifically forleveraging the Stable Diffusion model to generate domain specific images foundin medical imaging. We explore the sub-components of the Stable Diffusionpipeline (the variational autoencoder, the U-Net and the text-encoder) tofine-tune the model to generate medical images. We benchmark the efficacy ofthese efforts using quantitative image quality metrics and qualitativeradiologist-driven evaluations that accurately represent the clinical contentof conditional text prompts. Our best-performing model improves upon the stablediffusion baseline and can be conditioned to insert a realistic-lookingabnormality on a synthetic radiology image, while maintaining a 95% accuracy ona classifier trained to detect the abnormality.

Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains