Abstract：
Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm\'s effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.
摘要：
人工智能（AI）代理在使用新的数据批次进行顺序训练时会遇到灾难性的遗忘问题。这个问题阻碍了在涉及持续发展的任务中实施基于AI的模型，比如癌症预测。此外，整个幻灯片图像（WSI）在癌症管理中起着至关重要的作用，他们的自动分析在诊断过程中协助病理学家变得越来越受欢迎。增量学习（IL）技术旨在开发能够保留先前获取的信息的算法，同时还获得新的见解以预测未来的数据。深度IL技术需要解决WSI千兆像素规模带来的挑战，这通常需要使用多实例学习(MIL)框架。在本文中,我们介绍了一种专门用于分析MIL范式中WSI的IL算法。提出的多实例类增量学习（MICIL）算法首次将MIL与类IL相结合，允许在IL类方案中增量预测来自WSI的多种皮肤癌亚型。我们的框架结合了知识提炼和数据演练，随着一种新颖的嵌入级蒸馏，旨在保持聚合WSI水平的潜在空间。结果表明，该算法在解决平衡IL特定指标的挑战方面是有效的，比如不妥协和遗忘，解决了可塑性-稳定性的困境。
