metadata
license: cc
language:
- en
tags:
- self-supervised
- diffusion models
- mocov3
- simclrv2
- dino
- x-rays
- 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