Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.

ABSTRACT: The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high

SUBMITTER: Tang S 

PROVIDER: S-EPMC8052417 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature