多标签持续学习在医学领域：一个新基准
Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
AI总结：
针对医学影像多标签分类，提出结合新类到达与域转移的NIC基准（含NIH和CXP数据集、19类、7任务），并设计RCLP方法以最小遗忘实现优越性能。
摘要：
尽管医学领域在深度学习中至关重要，但该领域的大多数研究仅专注于在静态环境中训练模型。直到近年来，研究才开始关注动态环境，并通过持续学习（CL）技术解决灾难性遗忘问题。以往的研究主要集中于域增量学习和类增量学习等场景，这些场景未能完全捕捉真实世界应用的复杂性。因此，在本工作中，我们提出了一个新基准，通过考虑新实例和新类（NIC）场景，将新类到达和域转移的挑战结合在单一框架中。该基准旨在为医学影像中的多标签分类问题建模一个现实的持续学习设置。此外，与之前测试的场景相比，它包含更多的任务。具体而言，我们的基准包含两个数据集（NIH和CXP）、十九个类别和七个任务，这一流比之前测试的更长。为了解决CIL和NIC场景中常见的挑战（如任务推断问题），我们提出了一种名为回放巩固与标签传播（RCLP）的新方法。我们的方法超越了现有方法，以最小的遗忘表现出优越的性能。
英文摘要：
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in recent years that research has begun to address dynamic environments and tackle the Catastrophic Forgetting problem through Continual Learning (CL) techniques. Previous studies have primarily focused on scenarios such as Domain Incremental Learning and Class Incremental Learning, which do not fully capture the complexity of real-world applications. Therefore, in this work, we propose a novel benchmark combining the challenges of new class arrivals and domain shifts in a single framework, by considering the New Instances and New Classes (NIC) scenario. This benchmark aims to model a realistic CL setting for the multi-label classification problem in medical imaging. Additionally, it encompasses a greater number of tasks compared to previously tested scenarios. Specifically, our benchmark consists of two datasets (NIH and CXP), nineteen classes, and seven tasks, a stream longer than the previously tested ones. To solve common challenges (e.g., the task inference problem) found in the CIL and NIC scenarios, we propose a novel approach called Replay Consolidation with Label Propagation (RCLP). Our method surpasses existing approaches, exhibiting superior performance with minimal 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。
