用眼底图预测视力，给出可信赖的区间结果
Fundus Image-based Visual Acuity Assessment with PAC-Guarantees
- 基于眼底图像构建有概率保证的视力预测区间
- 在多个数据集上验证了预测区间覆盖率达标
- 适合需要高可信度医疗决策的临床场景
及时检测与治疗对维护眼健康至关重要。视觉敏锐度（VA）是衡量远距离视力清晰度的关键指标，对眼部健康管理具有重要意义。机器学习技术被引入辅助VA测量，有望减轻临床负担。然而，机器学习模型固有的不确定性使得仅依赖其进行VA预测不够理想。VA预测任务涉及多种不确定性来源，亟需更稳健的方法。一种有前景的方案是构建预测集或区间而非点估计，通过置信预测和可能近似正确（PAC）预测集等技术提供覆盖率保证。尽管潜力巨大，目前此类方法尚未应用于VA预测任务。为此，本文提出一种从眼底图像推导视觉敏锐度预测区间的算法，并提供PAC保证。实验结果表明，该方法满足PAC保证，性能可比或优于两个不提供此类保证的先前工作。
原文摘要 · Abstract (English)
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.
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