  Diffusion models (DMs) have emerged as powerful foundation models for a
variety of tasks, with a large focus in synthetic image generation. However,
their requirement of large annotated datasets for training limits their
applicability in medical imaging, where datasets are typically smaller and
sparsely annotated. We introduce DiNO-Diffusion, a self-supervised method for
training latent diffusion models (LDMs) that conditions the generation process
on image embeddings extracted from DiNO. By eliminating the reliance on
annotations, our training leverages over 868k unlabelled images from public
chest X-Ray (CXR) datasets. Despite being self-supervised, DiNO-Diffusion shows
comprehensive manifold coverage, with FID scores as low as 4.7, and emerging
properties when evaluated in downstream tasks. It can be used to generate
semantically-diverse synthetic datasets even from small data pools,
demonstrating up to 20% AUC increase in classification performance when used
for data augmentation. Images were generated with different sampling strategies
over the DiNO embedding manifold and using real images as a starting point.
Results suggest, DiNO-Diffusion could facilitate the creation of large datasets
for flexible training of downstream AI models from limited amount of real data,
while also holding potential for privacy preservation. Additionally,
DiNO-Diffusion demonstrates zero-shot segmentation performance of up to 84.4%
Dice score when evaluating lung lobe segmentation. This evidences good CXR
image-anatomy alignment, akin to segmenting using textual descriptors on
vanilla DMs. Finally, DiNO-Diffusion can be easily adapted to other medical
imaging modalities or state-of-the-art diffusion models, opening the door for
large-scale, multi-domain image generation pipelines for medical imaging.
