Roberto98/X-rays_Self-Supervised_Landmark_Detection

Official PyTorch pre-trained models of the paper: "Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images" (WACV 2025)

The models available include:

• Our DDPM pre-trained model at 6k, 8k, 8k iterations respectively for the Chest, Cephalometric and Hand dataset
• MocoV3 densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset
• SimClrV2 densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset
• Dino densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset

Citation

Accepted at WACV (Winter Conference on Applications of Computer Vision) 2025.

Bibtex

@article{DiVia2024,
  author = {Di Via, R. and Odone, F. and Pastore, V. P.},
  title = {Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images},
  year = {2024},
  journal = {arXiv},
  volume = {2407.18125},
  url = {https://arxiv.org/abs/2407.18125},
  note = {Submitted on 25 Jul 2024 (v1), last revised 29 Oct 2024 (this version, v2)}
}

APA

Di Via, R., Odone, F., & Pastore, V. P. (2024). Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images. ArXiv. https://arxiv.org/abs/2407.18125