Efficient automated error detection in medical data using deep-learning and label-clustering.

ABSTRACT: Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories-attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either rela

SUBMITTER: Nguyen TV 

PROVIDER: S-EPMC10638377 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature