Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

ABSTRACT: In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics -- a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing

SUBMITTER: Robinson C 

PROVIDER: S-EPMC7885941 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature