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🎉Introducing RoentGen, a generative vision-language foundation model based on #StableDiffusion, fine-tuned on a large chest x-ray and radiology report dataset, and controllable through text prompts!
@PierreChambon6 @Dr_ASChaudhari @curtlanglotz
🧵#Radiology #AI #StanfordAIMI
@PierreChambon6 @Dr_ASChaudhari @curtlanglotz
🧵#Radiology #AI #StanfordAIMI
#RoentGen is able to generate a wide variety of radiological chest x-ray (CXR) findings with fidelity and high level of detail. Of note, this is without being explicitly trained on class labels.
Built on previous work, #RoentGen is a fine-tuned latent diffusion model based on #StableDiffusion. Free-form medical text prompts are used to condition a denoising process, resulting in high-fidelity yet diverse CXR, improving on a typical limitation of GAN-based methods.
🎉 #StableDiffusion can be fine-tuned to generate medical images, and the outputs can be controlled using natural language text prompts!
In our latest work, we use SD to create synthetic chest xrays and insert pathologies like pleural effusions.
🧵 #Radiology #AI #StanfordAIMI
In our latest work, we use SD to create synthetic chest xrays and insert pathologies like pleural effusions.
🧵 #Radiology #AI #StanfordAIMI
#stablediffusion is a #LatentDiffusionModel and performs its generative tasks efficiently on low-dimensional representations of high-dimensional training inputs. SD's VAE latent space preserves relevant information contained in CXR; they can be reconstructed with high fidelity.
#StableDiffusion’s output can be controlled at inference time by using text prompts, but it is unclear how much medical imaging concepts SD incorporates. Simple text prompts show how hard it can be to get realistic-looking medical images out-of-the-box without specific training.
