Improving Intensive Care Chest X-Ray Classification by Transfer Learning and Automatic Label Generation

Radiologists commonly conduct chest X-rays for the diagnosis of pathologies or the evaluation of extrathoracic material positions in intensive care unit (ICU) patients. Automated assessments of radiographs have the potential to assist physicians by detecting pathologies that pose an emergency, leading to faster initiation of treatment and optimization of clinical workflows. The amount and quality of training data is a key aspect for developing deep learning models with reliable performance. This work investigates the effects of transfer learning on public data, automatically generated data labels and manual data annotation on the classification of ICU chest X-rays of the University Hospital Bonn.