Continual Learning for Domain Adaptation in Chest X-ray Classification

Understand

Over the last years, Deep Learning has been successfully applied to a broad range of medical applications.

• Especially in the context of chest X-ray classification, results have been reported which are on par, or even superior to experienced radiologists.
• Despite this success in controlled experimental environments, it has been noted that the ability of Deep Learning models to generalize to data from a new domain (with potentially different tasks) is often limited.
• In order to address this challenge, we investigate techniques from the field of Continual Learning (CL) including Joint Training (JT), Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF).