NCAdapt：采用领域特定神经细胞自动机的持续海马体分割动态自适应方法
NCAdapt: Dynamic adaptation with domain-specific Neural Cellular Automata for continual hippocampus segmentation
AI总结：
针对医学影像持续学习中模型适配新领域需保留旧知识的挑战，提出基于神经细胞自动机的NCAdapt方法，冻结骨干网络仅训练少量新增参数，在海马体分割任务上达到前沿性能。
摘要：
医学影像领域的持续学习（CL）面临独特挑战：模型需在适配新领域的同时保留先前习得的知识。本文提出NCAdapt，一种基于神经细胞自动机（NCA）的方法以应对该挑战。NCAdapt采用领域特定的多头结构，针对每个遇到的新领域，将可适配卷积层集成到NCA骨干网络中。初始训练后，NCA骨干被冻结，仅训练新增的、含384个参数的可适配卷积层，以及领域特定的NCA卷积层。我们在海马体分割任务上评估NCAdapt，将其性能与采用前沿（SOTA）持续学习方法的Lifelong nnU-Net和U-Net模型进行基准对比。我们的轻量方法取得了SOTA性能，凸显了其在解决医学影像持续学习挑战方面的有效性。论文录用后，我们将公开代码库，以支持可复现性并推动医学持续学习的进一步发展。
英文摘要：
Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt, a Neural Cellular Automata (NCA) based method designed to address this challenge. NCAdapt features a domain-specific multi-head structure, integrating adaptable convolutional layers into the NCA backbone for each new domain encountered. After initial training, the NCA backbone is frozen, and only the newly added adaptable convolutional layers, consisting of 384 parameters, are trained along with domain-specific NCA convolutions. We evaluate NCAdapt on hippocampus segmentation tasks, benchmarking its performance against Lifelong nnU-Net and U-Net models with state-of-the-art (SOTA) CL methods. Our lightweight approach achieves SOTA performance, underscoring its effectiveness in addressing CL challenges in medical imaging. Upon acceptance, we will make our code base publicly accessible to support reproducibility and foster further advancements in medical CL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
