arXiv:2606.20108v1 Announce Type: cross 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
The proliferation of deep learning applications in image analysis, especially in medical fields, necessitates robust and interpretable quality control methods, which this research addresses.
This development offers a method for explainable AI in image quality assessment, crucial for trust and adoption in sensitive applications like medical diagnostics, reducing reliance on subjective human labeling.
Traditional deep learning models for image quality assessment are often black boxes; this framework introduces transparency and spatial feedback without needing explicit quality labels.
- · Medical AI developers
- · Healthcare providers
- · AI explainability researchers
- · Computer vision engineers
- · Developers of opaque image quality assessment systems
- · Manual image quality control processes
Improved reliability and auditability of AI systems performing image quality assessment.
Faster and more consistent deployment of AI-powered diagnostic tools in clinical settings due to enhanced trustworthiness.
Reduced human workload in quality assurance, shifting roles towards oversight and anomaly resolution rather than granular review.
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Read at arXiv cs.LG