DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training
Keywords: Diffusion Models, Generative AI, Medical Imaging, Self-Supervision
TL;DR: Self-supervision is a viable strategy to train diffusion models in medical imaging, where annotations are scarce and/or fragmented
Abstract: Diffusion models (DMs) require large annotated datasets for training, limiting their applicability in medical imaging where datasets are typically smaller and sparsely annotated. We introduce DiNO-Diffusion, a self-supervised method for training DMs that conditions the generation process on image embeddings extracted from DiNO, a pretrained vision transformer. By not relying on annotations, our training leverages over 868k unlabelled images from public chest X-Ray (CXR) datasets. DiNO-Diffusion shows comprehensive manifold coverage, with FID scores as low as 4.7, and emerging properties when evaluated in downstream tasks, allowing 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. Results suggest that 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, evidencing good CXR image-anatomy alignment akin to textual descriptors on vanilla DMs. Finally, DiNO-Diffusion can be easily adapted to other medical imaging modalities or state-of-the-art diffusion models, allowing large-scale, multi-domain image generation pipelines for medical imaging.
Primary Area: generative models
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Submission Number: 9321
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