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  [Preprint](https://arxiv.org/abs/2507.17845) | [Code](https://github.com/bifold-pathomics/PathoROB) | [Licenses](#licenses) | [Cite](#how-to-cite)
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- **PathoROB is a benchmark for the robustness of pathology foundation models (FMs) to non-biological features from real-world medical center differences.**
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  <img src="pathorob_pipeline.png" alt="PathoROB pipeline" style="width: 95%;" align="center"/>
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- PathoROB contains **four datasets** covering 28 biological classes from 34 medical centers and **three metrics** for benchmarking FM robustness and its consequences for downstream applications:
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  1. **Robustness Index**: Measures the ability of an FM to capture biological features while ignoring
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  non-biological features.
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  2. **Average Performance Drop (APD)**: Measures the impact of non-biological features on the generalization performance of downstream models.
 
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  [Preprint](https://arxiv.org/abs/2507.17845) | [Code](https://github.com/bifold-pathomics/PathoROB) | [Licenses](#licenses) | [Cite](#how-to-cite)
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+ **PathoROB is a benchmark for the robustness of pathology foundation models (FMs) to non-biological features.**
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  <img src="pathorob_pipeline.png" alt="PathoROB pipeline" style="width: 95%;" align="center"/>
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+ PathoROB contains **four datasets** covering 28 biological classes from 34 medical centers and **three metrics**:
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  1. **Robustness Index**: Measures the ability of an FM to capture biological features while ignoring
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  non-biological features.
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  2. **Average Performance Drop (APD)**: Measures the impact of non-biological features on the generalization performance of downstream models.