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license:
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license: mit
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# EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
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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
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Currently you can access on GitHub https://github.com/penway/EFIQA.
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## Inference
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Test our model directly online at: https://huggingface.co/spaces/penway47/EFIQA. You can also download script and weight there.
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