Recent studies have made remarkable progress in histopathology classification. Based on current successes, contemporary works proposed to further upgrade the model towards a more generalizable and robust direction through incrementally learning from the sequentially delivered domains. Unlike previous parameter isolation based approaches that usually demand massive computation resources during model updating, we present a memory-efficient prompt tuning framework to cultivate model generalization potential in economical memory cost. For each incoming domain, we reuse the existing parameters of the initial classification model and attach lightweight trainable prompts into it for customized tuning. Considering the domain heterogeneity, we perform decoupled prompt tuning, where we adopt a domain-specific prompt for each domain to independently investigate its distinctive characteristics, and one domain-invariant prompt shared across all domains to continually explore the common content embedding throughout time. All domain-specific prompts will be appended to the prompt bank and isolated from further changes to prevent forgetting the distinctive features of early-seen domains. While the domain-invariant prompt will be passed on and iteratively evolve by style-augmented prompt refining to improve model generalization capability over time. In specific, we construct a graph with existing prompts and build a style-augmented graph attention network to guide the domain-invariant prompt exploring the overlapped latent embedding among all delivered domains for more domain generic representations. We have extensively evaluated our framework with two histopathology tasks, i.e., breast cancer metastasis classification and epithelium-stroma tissue classification, where our approach yielded superior performance and memory efficiency over the competing methods.

翻译：近期研究在组织病理学分类领域取得了显著进展。基于当前成果，近年来工作提出通过从序列交付领域中增量学习，进一步将模型向更具泛化性和鲁棒性的方向升级。不同于以往基于参数隔离的方法在模型更新时通常需要大量计算资源，我们提出了一种内存高效的提示调优框架，以经济的内存代价培养模型泛化潜力。针对每个新领域，我们复用初始分类模型的现有参数，并为其附加轻量级可训练提示进行定制化调优。考虑到领域异质性，我们采用解耦提示调优，即对每个领域采用独立领域特定提示以分别探究其独特特征，同时使用跨所有领域共享的领域不变提示持续探索时间维度上的共同内容嵌入。所有领域特定提示将追加至提示库，并隔离不再更改，以防止遗忘早期领域特征。领域不变提示则通过风格增强提示优化进行迭代演进，以提升模型随时间推移的泛化能力。具体而言，我们构建包含现有提示的图，并建立风格增强图注意力网络，引导领域不变提示探索所有交付领域间的重叠潜在嵌入，从而获取更具领域通用性的表示。我们在两项组织病理学任务（即乳腺癌转移分类和上皮-间质组织分类）上进行了广泛评估，结果表明我们的方法在性能和内存效率上均优于现有对比方法。