Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection
This addresses the issue of poor model performance due to incorrect labels, particularly in medical imaging, though it appears incremental as it builds on existing label error detection methods.
The paper tackles the problem of mislabeled data in machine learning classification by introducing Adaptive Label Error Detection (ALED), a method that detects mislabeled samples using a Bayesian approach, resulting in increased sensitivity without compromising precision on medical imaging datasets and enabling a 33.8% decrease in test set errors after fine-tuning.
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.