AI RESEARCH
Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study
arXiv CS.CV
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ArXi:2605.06160v1 Announce Type: new Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve over time. However, continual segmentation still faces three main challenges. First, the scenarios for this task remain insufficiently standardized for real-world clinical settings. Second, existing research has been primarily focused on mitigating forgetting, overlooking the other essential properties such as plasticity.