Image Feature Extraction
Transformers
PyTorch
xmag
feature-extraction
pathology
histopathology
foundation-model
distillation
dinov2
custom_code
Instructions to use AI4PATH/XMAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PATH/XMAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="AI4PATH/XMAG", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AI4PATH/XMAG", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Citation
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If you use this model, please cite:
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Su, Z., Akbar, A. R., & Niazi, M. K. K. (2025). Streamline pathology foundation model by cross-magnification distillation. arXiv preprint arXiv:2509.23097. Available at arXiv:2509.23097.
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## Citation
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If you use this model, please cite:
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Su, Z., Akbar, A. R., & Niazi, M. K. K. (2025). Streamline pathology foundation model by cross-magnification distillation. arXiv preprint arXiv:2509.23097. Available at arXiv:2509.23097.
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```
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@article{su2025streamline,
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title={Streamline pathology foundation model by cross-magnification distillation},
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author={Su, Ziyu and Akbar, Abdul Rehman and Sajjad, Usama and Parwani, Anil V and Niazi, Muhammad Khalid Khan},
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journal={arXiv preprint arXiv:2509.23097},
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year={2025}
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}
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```
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