Healthcare clinics regularly encounter dynamic data that changes due to
variations in patient populations, treatment policies, medical devices, and
emerging disease patterns. Deep learning models can suffer from catastrophic
forgetting when fine-tuned in such scenarios, causing poor performance on
previously learned tasks. Continual learning allows learning on new tasks
without performance drop on previous tasks. In this work, we investigate the
performance of continual learning models on four different medical imaging
scenarios involving ten classification datasets from diverse modalities,
clinical specialties, and hospitals. We implement various continual learning
approaches and evaluate their performance in these scenarios. Our results
demonstrate that a single model can sequentially learn new tasks from different
specialties and achieve comparable performance to naive methods. These findings
indicate the feasibility of recycling or sharing models across the same or
different medical specialties, offering another step towards the development of
general-purpose medical imaging AI that can be shared across institutions.

本研究通过评估四个不同医学图像场景中各种连续学习方法的性能表明，单一模型可以顺序地学习来自不同专业的新任务，并达到与传统方法相当的性能，从而证明了在医学领域跨机构共享或回收模型的可行性，进一步推动了通用医学影像人工智能的发展。