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

@InProceedings{Di_Via_2025_WACV,
    author    = {Di Via, Roberto and Odone, Francesca and Pastore, Vito Paolo},
    title     = {Self-Supervised Pre-Training with Diffusion Model for Few-Shot Landmark Detection in X-Ray Images},
    booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
    month     = {February},
    year      = {2025},
    pages     = {3886-3896}
}

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