Continual Segmentation

Continual segmentation aims to enable deep learning models to learn new image segmentation tasks sequentially without forgetting previously acquired knowledge, a crucial challenge in domains with evolving data streams like medical imaging. Current research focuses on mitigating "catastrophic forgetting" through techniques such as knowledge distillation, replay strategies (including distribution-aware and diffusion-based methods), and architectural innovations like Mixture-of-Experts and dynamic query mechanisms. These advancements are vital for building robust and adaptable segmentation models in resource-constrained environments and for applications where continuous model updates are necessary, such as medical diagnosis and autonomous systems.