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README.md
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library_name: transformers
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# Model card for RAD-DINO
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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 section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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Some potential uses are:
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- Image classification, with a classifier trained on top of the `CLS` token
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- Image segmentation, with a decoder trained using the patch tokens
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- Image retrieval, using nearest neighbors of the CLS token
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- Report generation, with a language model to decode text
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Fine-tuning RAD-DINO is typically not necessary to obtain good performance in downstream tasks.
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### Out-of-scope use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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This model is shared for research purposes only.
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It is not meant to be used for clinical practice.
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## Bias, risks, and limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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We used images from five public, deidentified chest X-ray datasets to train this checkpoint of RAD-DINO.
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Images in the validation and test sets
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| Dataset | Num. images |
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| --------- | ----------: |
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## Model card contact
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Fernando Pérez-García ([`fperezgarcia@microsoft.com`](mailto:fperezgarcia@microsoft.com)).
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library_name: transformers
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license: mit
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---
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# Model card for RAD-DINO
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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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RAD-DINO is shared for research purposes only.
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It is **not meant to be used for clinical practice**.
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<!-- ### Downstream use -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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The model is a vision backbone that can be plugged to other models for downstream tasks.
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Some potential uses are:
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- Image classification, with a classifier trained on top of the `CLS` token
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- Image segmentation, with a decoder trained using the patch tokens
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- Clustering, using the image embeddings directly
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- Image retrieval, using nearest neighbors of the CLS token
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- Report generation, with a language model to decode text
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Fine-tuning RAD-DINO is typically not necessary to obtain good performance in downstream tasks.
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<!-- ### Out-of-scope use -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, risks, and limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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We used images from five public, deidentified chest X-ray datasets to train this checkpoint of RAD-DINO.
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Images in the validation and test sets used to train [MAIRA](https://arxiv.org/abs/2311.13668) were excluded from the training set of RAD-DINO.
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The list of image files used for training is available at [`./training_images.csv`](./training_images.csv).
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| Dataset | Num. images |
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| --------- | ----------: |
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## Model card contact
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Fernando Pérez-García ([`fperezgarcia@microsoft.com`](mailto:fperezgarcia@microsoft.com)).
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