利用旧知识在医学图像中持续学习新类别
Leveraging Old Knowledge to Continually Learn New Classes in Medical Images
AI总结：
本文提出一种利用旧知识进行医学图像类别增量持续学习的框架，通过动态扩展表示和双目标交替训练，在多个医学数据集上取得了优于现有基线的准确率与遗忘表现。
摘要：
类别增量持续学习是开发人工智能系统的核心步骤，这类系统能够通过在不遗忘先前所学概念的情况下学习新概念，持续适应环境变化。在医学领域尤其需要这种能力，因为需要持续从新到达的数据中学习，以对扩展的疾病集合进行分类。本文关注如何利用旧知识来学习新类别而不发生灾难性遗忘。我们提出了一个包含两个主要组成部分的框架：（1）一种具有扩展表示的动态架构，用于保留先前学习的特征并容纳新特征；（2）一种在两个目标之间交替进行的训练过程，以平衡新特征的学习，同时保持模型在旧类别上的性能。在多个医学数据集上的实验结果表明，我们的解决方案在类别准确率和遗忘率方面均优于最先进的基线方法。
英文摘要：
Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new concepts without forgetting those previously learned. This is especially needed in the medical domain where continually learning from new incoming data is required to classify an expanded set of diseases. In this work, we focus on how old knowledge can be leveraged to learn new classes without catastrophic forgetting. We propose a framework that comprises of two main components: (1) a dynamic architecture with expanding representations to preserve previously learned features and accommodate new features; and (2) a training procedure alternating between two objectives to balance the learning of new features while maintaining the model's performance on old classes. Experiment results on multiple medical datasets show that our solution is able to achieve superior performance over state-of-the-art baselines in terms of class accuracy and forgetting.
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。
