通过标准化损失聚合，自动识别医学影像中的噪声标签。
Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
- 用重复交叉验证的标准化损失聚合，连续估算样本标签可靠性。
- 在不同噪声水平下均优于传统计数法，低噪声时收敛更快。
- 适合需高质量标注的医学图像分类任务，辅助高效重标注。
由于观察者差异和病例模糊性，大规模医学影像数据集普遍存在噪声标签。本文提出一种统计基础、任务无关的样本级噪声标签检测框架——标准化损失聚合（SLA）。SLA通过聚合多次交叉验证中折级别的标准化验证损失，量化标签可靠性。该方法将离散的硬计数方案推广为连续估计器，同时捕捉性能偏差的频率与幅度，生成可解释且统计稳定的噪声评分。在公开的眼底图像数据集上的实验表明，SLA在所有噪声水平下均持续优于硬计数基线，尤其在低噪声比条件下，细微损失变化具有信息量，收敛速度显著更快。高SLA得分样本提示可能存在歧义或标注错误，可指导高效重标注，提升任意分类任务的数据集可靠性。
原文摘要 · Abstract (English)
Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framework, Standardized Loss Aggregation (SLA), for detecting noisy labels at the sample level. SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores. Experiments on a public fundus dataset demonstrate that SLA consistently outperforms the hard-counting baseline across all noise levels and converges substantially faster, especially under low noise ratios where subtle loss variations are informative. Samples with high SLA scores indicate potentially ambiguous or mislabeled cases, guiding efficient re-annotation and improving dataset reliability for any classification task.
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