Generate Medical Images Using a Prompt
Multi-modal foundation model to create radiology images using text
Overview
There are many rare diseases out there and many of them are not only untreatable but also difficult for doctors to diagnose. Due to rarity of the condition, not many medical images are available. In addition, although there are many medical images out there, there is not enough good quality labeled images. It is expensive and laborious to label medical images. Synthetic data could be an alternative for rare conditions and to generate good quality images. *Stable Diffusion* is a text-to-image model where you provide a text prompt to generate an image. We can adapt Stable Diffusion from general-purpose to domain-specific.
How does it work?
With the help of Amazon SageMaker and it’s native integration with HuggingFace, we can fine-tune general purpose Stable Diffusion model to domain-specific model such as radiology. We used ROCO dataset that contains radiology images along with radiology report to train multi-modal model for image generation using text.
Demo prompts
- The patient had residual paralysis of the hand after poliomyelitis. It was necessary to stabilize the thumb with reference to the index finger. This was accomplished by placing a graft from the bone bank between the first and second metacarpals. The roentgenogram shows the complete healing of the graft one year later.
• Showing the subtrochanteric fracture in the porotic bone.
• There are more examples available in the demo app.
Architecture
Stable diffusion has main three components:
• Text encoder that turns natural language prompts into 768 dimensional vectors.
• Variational autoencoder generates output from denoised image to an image.
• Denoising U-Net model which turns random noise to images in latent space.
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