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Whole slide image (WSI) analysis has become increasingly important in themedical 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. Inthis paper, we propose the FIRST continual learning framework for WSI analysis,named ConSlide, to tackle the challenges of enormous image size, utilization ofhierarchical structure, and catastrophic forgetting by progressive modelupdating on multiple sequential datasets. Our framework contains three keycomponents. The Hierarchical Interaction Transformer (HIT) is proposed to modeland utilize the hierarchical structural knowledge of WSI. TheBreakup-Reorganize (BuRo) rehearsal method is developed for WSI data replaywith efficient region storing buffer and WSI reorganizing operation. Theasynchronous updating mechanism is devised to encourage the network to learngeneric and specific knowledge respectively during the replay stage, based on anested cross-scale similarity learning (CSSL) module. We evaluated the proposedConSlide on four public WSI datasets from TCGA projects. It performs best overother state-of-the-art methods with a fair WSI-based continual learning settingand achieves a better trade-off of the overall performance and forgetting onprevious task

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis