  Despite the critical importance of the medical domain in Deep Learning, most
of the research in this area solely focuses on training models in static
environments. It is only in recent years that research has begun to address
dynamic environments and tackle the Catastrophic Forgetting problem through
Continual Learning (CL) techniques. Previous studies have primarily focused on
scenarios such as Domain Incremental Learning and Class Incremental Learning,
which do not fully capture the complexity of real-world applications.
Therefore, in this work, we propose a novel benchmark combining the challenges
of new class arrivals and domain shifts in a single framework, by considering
the New Instances and New Classes (NIC) scenario. This benchmark aims to model
a realistic CL setting for the multi-label classification problem in medical
imaging. Additionally, it encompasses a greater number of tasks compared to
previously tested scenarios. Specifically, our benchmark consists of two
datasets (NIH and CXP), nineteen classes, and seven tasks, a stream longer than
the previously tested ones. To solve common challenges (e.g., the task
inference problem) found in the CIL and NIC scenarios, we propose a novel
approach called Replay Consolidation with Label Propagation (RCLP). Our method
surpasses existing approaches, exhibiting superior performance with minimal
forgetting.
