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

arXiv: https://arxiv.org/abs/2407.18125 · HTML: https://mlsanity.com/paper/2407.18125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [You Only Learn Once: Universal Anatomical Landmark Detection](https://mlsanity.com/paper/2103.04657/) (2021)
- [Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection](https://mlsanity.com/paper/2007.05225/) (2020)
- [Decoder Denoising Pretraining for Semantic Segmentation](https://mlsanity.com/paper/2205.11423/) (2022)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks](https://mlsanity.com/paper/1907.12237/) (2019)
- [UOD: Universal One-shot Detection of Anatomical Landmarks](https://mlsanity.com/paper/2306.07615/) (2023)
- [One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement](https://mlsanity.com/paper/2208.00453/) (2022)
- [Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?](https://mlsanity.com/paper/2403.01470/) (2024)
- [FM-OSD: Foundation Model-Enabled One-Shot Detection of Anatomical Landmarks](https://mlsanity.com/paper/2407.05412/) (2024)
- [Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs](https://mlsanity.com/paper/2206.02087/) (2022)
- [Pre-Training with Diffusion models for Dental Radiography segmentation](https://mlsanity.com/paper/2307.14066/) (2023)

## Built on by (most cited first, 2 of 2)

- [Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray](https://mlsanity.com/paper/2601.18555/) (2026)
- [CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection](https://mlsanity.com/paper/2606.04898/) (2026)

## Abstract (arXiv)

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.