标题： 双对齐知识保留用于持续医学图像分割 显示英文标题

标题： Dual-Alignment Knowledge Retention for Continual Medical Image Segmentation

摘要： 持续学习在医学图像分割中涉及跨不同领域的顺序数据获取（例如，临床地点），其中过去和当前领域之间的任务干扰常常导致灾难性遗忘。 现有的持续学习方法无法捕捉任务之间的复杂依赖关系。 我们引入了一个新框架，通过在当前任务中建立和增强历史数据与网络之间的复杂依赖关系来减轻遗忘。 我们的框架具有双对齐策略，跨网络对齐（CNA）模块分别对齐当前网络和之前网络的瓶颈层提取的特征，而跨表示对齐（CRA）模块分别对齐当前网络从历史缓冲数据和当前输入数据中学习的特征。 实现这两种对齐是一项非平凡的任务。 为了解决这个问题，我们进一步分析了广为人知的希尔伯特-施密特独立准则（HSIC）的线性和非线性形式，并在CRA模块中专门设计了特征映射和特征配对块。 在医学图像分割任务上的实验表明，我们的框架在领域变化下有效缓解了灾难性遗忘。 显示英文摘要

摘要： Continual learning in medical image segmentation involves sequential data acquisition across diverse domains (e.g., clinical sites), where task interference between past and current domains often leads to catastrophic forgetting. Existing continual learning methods fail to capture the complex dependencies between tasks. We introduce a novel framework that mitigates forgetting by establishing and enhancing complex dependencies between historical data and the network in the present task. Our framework features a dual-alignment strategy, the cross-network alignment (CNA) module aligns the features extracted from the bottleneck layers of the current and previous networks, respectively, while the cross-representation alignment (CRA) module aligns the features learned by the current network from historical buffered data and current input data, respectively. Implementing both types of alignment is a non-trivial task. To address this, we further analyze the linear and nonlinear forms of the well-established Hilbert-Schmidt Independence Criterion (HSIC) and deliberately design feature mapping and feature pairing blocks within the CRA module. Experiments on medical image segmentation task demonstrate our framework's effectiveness in mitigating catastrophic forgetting under domain shifts.