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--- |
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# Model Card for RETFound_MAE_MEH |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard aims to provide a pre-trained vision foundation model [RETFound](https://github.com/rmaphoh/RETFound_MAE), pre-trained with DINOV2 on a part of [SDPP data](https://www.nature.com/articles/s41591-023-02702-z). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Yukun Zhou |
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- **Model type:** Pre-trained model |
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- **License:** Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0) |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [RETFound](https://github.com/rmaphoh/RETFound_MAE) |
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- **Paper:** [Nature paper](https://www.nature.com/articles/s41586-023-06555-x) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This repo contains the model weight. After granted the access, please fill the token in the [code](https://github.com/rmaphoh/RETFound_MAE). |
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The code will automatically download the model and run the training. |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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- **Hardware Type:** 4 * NVIDIA A100 80GB |
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- **Hours used:** 14 days |
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- **Cloud Provider:** UCL CS Cluster & Shanghai Jiaotong University Cluster |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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``` |
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@article{zhou2023foundation, |
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title={A foundation model for generalizable disease detection from retinal images}, |
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author={Zhou, Yukun and Chia, Mark A and Wagner, Siegfried K and Ayhan, Murat S and Williamson, Dominic J and Struyven, Robbert R and Liu, Timing and Xu, Moucheng and Lozano, Mateo G and Woodward-Court, Peter and others}, |
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journal={Nature}, |
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volume={622}, |
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number={7981}, |
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pages={156--163}, |
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year={2023}, |
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publisher={Nature Publishing Group UK London} |
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} |
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``` |
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## Model Card Contact |
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**[email protected]** or **[email protected]** |
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