EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors

Pengwei Wang, José Morano, Qian Wan, Hrvoje Bogunović

Keywords: Image Quality Assessment, Unsupervised Anomaly Detection, Explainability, Ophthalmology, Color Fundus Photography

TL;DR: We ground fundus quality assessment in anatomical priors rather than subjective labels, enabling an unsupervised approach with spatial explainability and robust cross-dataset generalization.

Abstract: Image quality control is vital for a wide range of downstream applications. Deep learning based image quality assessment methods typically train classifiers on dataset-specific quality labels, inheriting two limitations: (1) generalization is tied to the labeling criteria of the training set, and (2) these methods cannot provide spatial feedback on where the quality is degraded, lacking explainability. In this work, we propose EFIQA, a framework that requires no quality-related supervision and produces spatial quality maps by design. Rather than learning "what is degradation" from human-annotated labels, EFIQA learns "what should be there" by leveraging anatomical priors. For fundus photography, we instantiate this as a two-stage approach, by first training an unsupervised anomaly detector via masked anatomical inpainting to identify regions of missing vasculature, and then distilling this prior knowledge into a shallow adapter mapping features of a frozen foundation model to precise quality maps. External-dataset evaluation demonstrates that this label-free approach with minimal adaptation achieves better performance and explainability compared with supervised methods across benchmarks with different quality criteria, highlighting its potential for real-world applications.

Primary Subject Area: Interpretability and Explainable AI

Secondary Subject Area: Application: Ophthalmology

Registration Requirement: Yes

Reproducibility: https://github.com/penway/EFIQA

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Originality Policy: Yes

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Submission Number: 182