File size: 1,421 Bytes
172567c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
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
```
@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
``` |