  In the last few years, deep neural networks have been extensively applied in
the medical domain for different tasks, ranging from image classification and
segmentation to landmark detection. However, the application of these
technologies in the medical domain is often hindered by data scarcity, both in
terms of available annotations and images. This study introduces a new
self-supervised pre-training protocol based on diffusion models for landmark
detection in x-ray images. Our results show that the proposed self-supervised
framework can provide accurate landmark detection with a minimal number of
available annotated training images (up to 50), outperforming ImageNet
supervised pre-training and state-of-the-art self-supervised pre-trainings for
three popular x-ray benchmark datasets. To our knowledge, this is the first
exploration of diffusion models for self-supervised learning in landmark
detection, which may offer a valuable pre-training approach in few-shot
regimes, for mitigating data scarcity.
