用贝叶斯方法检测图像数据中的标注错误，提升模型准确性。
Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection
- 从深度网络提取特征，用高斯分布建模每类数据流形。
- 在多个医学影像数据集上检测准确率显著提升，误报率不变。
- 适合需要高质量标注的医疗图像分析场景，可直接使用工具包。
机器学习分类系统在训练数据存在错误标签时性能会下降，即使数据由专家标注。随着机器学习广泛应用，识别并修正标注错误变得愈发重要。本文提出自适应标签错误检测（ALED），该方法从深度卷积神经网络中提取中间特征，对特征去噪后，用多维高斯分布建模每一类的低维流形，并通过简单的似然比检验识别错误标注样本。实验表明，ALED相比现有方法显著提高了检测灵敏度，同时保持精度不变，在多个医学影像数据集上表现优异。一个实例显示，使用修正后的数据微调模型后，测试集错误率降低了33.8%，为终端用户带来显著收益。ALED已集成于Python工具包statlab中。
原文摘要 · Abstract (English)
Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As machine learning becomes more widespread, it is increasingly imperative to identify and correct mislabeling to develop more powerful models. In this work, we motivate and describe Adaptive Label Error Detection (ALED), a novel method of detecting mislabeling. ALED extracts an intermediate feature space from a deep convolutional neural network, denoises the features, models the reduced manifold of each class with a multidimensional Gaussian distribution, and performs a simple likelihood ratio test to identify mislabeled samples. We show that ALED has markedly increased sensitivity, without compromising precision, compared to established label error detection methods, on multiple medical imaging datasets. We demonstrate an example where fine-tuning a neural network on corrected data results in a 33.8% decrease in test set errors, providing strong benefits to end users. The ALED detector is deployed in the Python package statlab.
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