Fundus Image-based Visual Acuity Assessment with PAC-Guarantees

TL;DR

This paper introduces a PAC-guaranteed method for deriving prediction intervals for visual acuity estimation from fundus images, enhancing reliability over existing approaches without such guarantees.

Contribution

It applies PAC prediction sets to fundus image-based VA assessment, providing coverage guarantees previously unaddressed in this domain.

Findings

PAC guarantees are upheld in VA prediction.

Performance is comparable or better than prior non-guaranteed methods.

The approach enhances robustness and reliability of VA predictions.

Abstract

Timely detection and treatment are essential for maintaining eye health. Visual acuity (VA), which measures the clarity of vision at a distance, is a crucial metric for managing eye health. Machine learning (ML) techniques have been introduced to assist in VA measurement, potentially alleviating clinicians' workloads. However, the inherent uncertainties in ML models make relying solely on them for VA prediction less than ideal. The VA prediction task involves multiple sources of uncertainty, requiring more robust approaches. A promising method is to build prediction sets or intervals rather than point estimates, offering coverage guarantees through techniques like conformal prediction and Probably Approximately Correct (PAC) prediction sets. Despite the potential, to date, these approaches have not been applied to the VA prediction task.To address this, we propose a method for deriving prediction intervals for estimating visual acuity from fundus images with a PAC guarantee. Our experimental results demonstrate that the PAC guarantees are upheld, with performance comparable to or better than that of two prior works that do not provide such guarantees.