Summary of Self-supervised Pre-training with Diffusion Model For Few-shot Landmark Detection in X-ray Images, by Roberto Di Via et al.
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images
by Roberto Di Via, Francesca Odone, Vito Paolo Pastore
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel application of denoising diffusion probabilistic models (DDPMs) to landmark detection in x-ray imaging, addressing the challenge of limited annotated data. The key innovation is leveraging DDPMs for self-supervised pre-training, enabling accurate landmark detection with minimal annotated training data (as few as 50 images). This method surpasses ImageNet supervised pre-training and traditional self-supervised techniques across three popular x-ray benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses denoising diffusion probabilistic models to improve landmark detection in x-ray imaging. It’s a new way to use computers to find important points on x-rays without needing lots of labeled training data. This can be helpful when there isn’t much data available, which is often the case with medical images. |
Keywords
» Artificial intelligence » Diffusion » Self supervised » Supervised
