Tech 14 Feb 2023

Stable diffusion could solve a gap in medical imaging data

Medical doctors who specialize in rare diseases get only so many opportunities to learn as they go. The lack of diverse healthcare data to train students is a key challenge in these fields.“When you are working in a setting with scarce data, your performance correlates with experience — the more images you see, the better you become,” said Christian Bluethgen, a thoracic radiologist and Stanford Center for AI in Medicine and Imaging (AIMI) postdoc researcher who has studied rare lung diseases for the last seven years.When Stability AI released Stable Diffusion, its text-to-image foundation model, to the public in August, Bluethgen had an idea: What if you could combine a real need in medicine with the ease of creating beautiful images from simple text prompts? If Stable Diffusion could create medical images that accurately depict the clinical context, it could alleviate the gap in training data.Bluethgen teamed up with Pierre Chambon, a Stanford graduate student at the Institute for Computational and Mathematical Engineering and machine learning (ML) researcher at AIMI, to design a study that would seek to expand the capabilities of Stable Diffusion to generate the most common type of medical images — chest X-rays.