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--- |
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license: cc |
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language: |
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- en |
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tags: |
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- self-supervised |
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- diffusion models |
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- mocov3 |
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- simclrv2 |
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- dino |
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- x-rays |
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- landmark detection |
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--- |
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# 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) |
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The models available include: |
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- Our DDPM pre-trained model at 6k, 8k, 8k iterations respectively for the Chest, Cephalometric and Hand dataset |
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- MocoV3 densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset |
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- SimClrV2 densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset |
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- Dino densenet161 model at 10k iterations for the Chest, Cephalometric and Hand dataset |
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# Citation |
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Accepted at WACV (Winter Conference on Applications of Computer Vision) 2025. |
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### Bibtex |
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``` |
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@InProceedings{Di_Via_2025_WACV, |
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author = {Di Via, Roberto and Odone, Francesca and Pastore, Vito Paolo}, |
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title = {Self-Supervised Pre-Training with Diffusion Model for Few-Shot Landmark Detection in X-Ray Images}, |
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booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, |
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month = {February}, |
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year = {2025}, |
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pages = {3886-3896} |
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} |
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``` |
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### APA |
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``` |
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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 |
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``` |
