Multi-label image classification in dynamic environments is a problem that
poses significant challenges. 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. In this
paper, we study the problem of classification of medical imaging in the
scenario termed New Instances \& New Classes, which combines the challenges of
both new class arrivals and domain shifts in a single framework. Unlike
traditional scenarios, it reflects the realistic nature of CL in domains such
as medical imaging, where updates may introduce both new classes and changes in
domain characteristics. To address the unique challenges posed by this complex
scenario, we introduce a novel approach called Pseudo-Label Replay. This method
aims to mitigate forgetting while adapting to new classes and domain shifts by
combining the advantages of the Replay and Pseudo-Label methods and solving
their limitations in the proposed scenario. % part3 We evaluate our proposed
approach on a challenging benchmark consisting of two datasets, seven tasks,
and nineteen classes, modeling a realistic Continual Learning scenario. Our
experimental findings demonstrate the effectiveness of Pseudo-Label Replay in
addressing the challenges posed by the complex scenario proposed. Our method
surpasses existing approaches, exhibiting superior performance while showing
minimal forgetting.

医学成像的分类问题具有新类别和域偏移的挑战，本文提出了一种名为伪标签重播的新方法，通过结合重播和伪标签方法的优势以解决现有的限制，有效地应对挑战并展现卓越性能和最小的遗忘。