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| license: mit |
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| # EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors |
| We ground fundus quality assessment in anatomical integrity rather than subjective labels, enabling an unsupervised approach with spatial explainability and robust cross-dataset generalization. |
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| Currently under review in MIDL2026: https://openreview.net/forum?id=b9TBF3O88T |
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| ## Code |
| Currently you can access on GitHub https://github.com/penway/EFIQA. |
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| ## Inference |
| Test our model directly online at: https://huggingface.co/spaces/penway47/EFIQA. You can also download script and weight there. |
