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

Abstract

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.

Segmentation and Analysis of Lumbar Spine MRI Scans for Vertebral Body Measurements

“…AI (artificial intelligence) and CV (computer vision) tools can support radiologists with these challenges. Physicians can be aided in their diagnosis by AI tools to optimize the workflow without compromising the quality of analysis or quality of patient care [1,3,4].…”