The paper titled Beware of diffusion models for synthesizing medical images highlights a potential risk associated with diffusion models — they might memorize and reproduce training images, an issue particularly amplified in small datasets or when images are extracted as 2D slices from 3D volumes.

• Analyzed the correlation between synthetic images and training data using StyleGAN and diffusion models with datasets like BRATS20, BRATS21, and chest x-ray pneumonia sets.
• Showcased that diffusion models tend to memorize training data more than GANs like StyleGAN.
• Calls for cautious use of diffusion models in medical imaging to avoid unintended sharing of real patient data in synthetic form.

The findings call attention to the need for careful consideration when employing diffusion models for medical purposes, as they may jeopardize data privacy and introduce a risk of leaking sensitive patient information.