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.

本研究解决了医学领域中标记点检测面临的数据稀缺问题，提出了一种基于扩散模型的自监督预训练协议。研究结果表明，该方法在减少标注训练图像（最多50张）的情况下，能在三个流行的X光基准数据集上实现高准确度的标记点检测，超越了现有的监督预训练和最先进的自监督预训练方法。

基于扩散模型的自监督预训练用于X光图像中的少量标记点检测