Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images

TL;DR

This paper introduces a novel self-supervised pre-training method using diffusion models for landmark detection in x-ray images, significantly reducing the need for annotated data and outperforming traditional methods.

Contribution

It is the first to apply diffusion models for self-supervised learning in landmark detection, enabling effective training with minimal annotated data in medical imaging.

Findings

Achieves accurate landmark detection with as few as 50 annotated images.

Outperforms ImageNet supervised pre-training and traditional self-supervised methods.

Effective in few-shot learning scenarios for x-ray landmark detection.

Abstract

Deep neural networks have been extensively applied in the medical domain for various tasks, including image classification, segmentation, and landmark detection. However, their application is often hindered by data scarcity, both in terms of available annotations and images. This study introduces a novel application of denoising diffusion probabilistic models (DDPMs) to the landmark detection task, specifically addressing the challenge of limited annotated data in x-ray imaging. Our key innovation lies in leveraging DDPMs for self-supervised pre-training in landmark detection, a previously unexplored approach in this domain. This method enables accurate landmark detection with minimal annotated training data (as few as 50 images), surpassing both ImageNet supervised pre-training and traditional self-supervised techniques across three popular x-ray benchmark datasets. To our knowledge, this work represents the first application 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.