Whole slide image (WSI) analysis has become increasingly important in the medical imaging community, enabling automated and objective diagnosis, prognosis, and therapeutic-response prediction. However, in clinical practice, the ever-evolving environment hamper the utility of WSI analysis models. In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets. Our framework contains three key components. The Hierarchical Interaction Transformer (HIT) is proposed to model and utilize the hierarchical structural knowledge of WSI. The Breakup-Reorganize (BuRo) rehearsal method is developed for WSI data replay with efficient region storing buffer and WSI reorganizing operation. The asynchronous updating mechanism is devised to encourage the network to learn generic and specific knowledge respectively during the replay stage, based on a nested cross-scale similarity learning (CSSL) module. We evaluated the proposed ConSlide on four public WSI datasets from TCGA projects. It performs best over other state-of-the-art methods with a fair WSI-based continual learning setting and achieves a better trade-off of the overall performance and forgetting on previous task

翻译：全切片图像分析在医学影像领域日益重要，可实现自动化、客观的诊断、预后及治疗反应预测。然而，临床实践中不断演变的环境限制了全切片图像分析模型的应用。本文提出首个面向全切片图像分析的持续学习框架ConSlide，通过在多连续数据集上进行渐进式模型更新，应对图像尺寸巨大、层级结构利用、灾难性遗忘等挑战。该框架包含三个核心组件：层级交互Transformer用于建模和利用全切片图像的层级结构知识；分裂重组回放方法通过高效区域存储缓冲区和全切片重组操作实现数据重放；基于嵌套跨尺度相似性学习模块的异步更新机制，在重放阶段分别引导网络学习通用知识与特异性知识。我们在TCGA项目四个公开全切片图像数据集上评估ConSlide，在公平的全切片图像持续学习设定下，该方法优于其他最新技术，并在整体性能与旧任务遗忘抑制间实现了更优平衡。