FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images

2026-06-24

AI Summary

Current fundus image quality assessment methods predominantly rely on image-level labels, which struggle to quantify localized degradations and often lack task-agnosticism and interpretability. To address these limitations, this work proposes a pixel-level quality assessment approach grounded in the visibility of anatomical structures. The authors introduce FunPiQ, the first pixel-level annotated benchmark for fundus image quality, and develop EFIQA-CP, an inherently interpretable-by-design CNN model. Leveraging non-negative positive-unlabeled learning to generate high-quality pseudo-labels for training, the proposed method significantly outperforms both post-hoc explanation of classification models and anomaly detection baselines across multiple experiments, thereby demonstrating the efficacy and superiority of pixel-level quality evaluation.

Abstract

Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screening. However, it is highly susceptible to degradations, making robust fundus image quality assessment (FIQA) crucial. The criteria for what constitutes high-quality at the image level vary across clinical tasks, making FIQA dependent on expert knowledge. This motivated the development of automated methods and datasets. While existing datasets aim to standardize image-level quality, their criteria often differ. Furthermore, image-level labels preclude the quantitative evaluation of localized degradations, which is essential for trustworthy FIQA. We argue that pixel-level FIQA based on anatomical visibility represents a more task-agnostic, explainable approach. In this work, we introduce FunPiQ, the first FIQA benchmark to provide pixel-level quality annotations. In addition, we propose EFIQA-CP, an explainable-by-design (EBD) method that uses quality pseudo-labels based on anatomical visibility to train a CNN via Non-Negative Positive-Unlabeled learning. Extensive evaluations of classification methods with post-hoc explanations, anomaly detection methods, and EBD methods demonstrate the superior performance of the last and, particularly, of EFIQA-CP.

Problem

• fundus image quality assessment
• pixel-level quality
• anatomical visibility
• explainable AI
• image degradation

Innovation

• pixel-level quality assessment
• explainable-by-design
• fundus image quality assessment
• anatomical visibility
• Non-Negative Positive-Unlabeled learning