Can Machine Learning Be Better than Biased Readers?

ABSTRACT: Background: Training machine learning (ML) models in medical imaging requires large amounts of labeled data. To minimize labeling workload, it is common to divide training data among multiple readers for separate annotation without consensus and then combine the labeled data for training a ML model. This can lead to a biased training dataset and poor ML algorithm prediction performance. The purpose of this study is to determine if ML algorithms can overcome biases caused by multiple readers' labeling without consensus. Methods: This study used a publicly available chest X-ray dataset of pediatric pneumonia. As an analogy to a practical dataset without labeling consensus among multiple readers, random and systematic errors were artificially added to the dataset to generate bia

SUBMITTER: Hibi A 

PROVIDER: S-EPMC10204355 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature