Deep learning methods towards clinically applicable Chest X-ray interpretation systems

E. Çallı

  • Promotor: B. van Ginneken
  • Copromotor: K. Murphy
  • Graduation year: 2023
  • Radboud University, Nijmegen


Chest X-Ray is a very commonly acquired, low-cost imaging examination which is sensitive for multiple pathologies while exposing the patient to very little ionizing radiation. The number of medical imaging examinations being acquired increases year-on-year while there is a global shortage of radiologists qualified to interpret the images. This research presents findings on the use of deep learning for interpretation of chest X-Ray images. An in-depth review of the current state-of-the-art is provided as well as investigation of handling of label noise, outlier detection, explainable identification of emphysema and detection of COVID-19 with robustness to missing data.