AI 中文总结
针对眼科领域增量学习中的标签分布偏移与类别不平衡，提出ToRe框架，采用参数隔离与令牌自适应递归，增强少数类特征，在九个数据集上优于现有方法且近零遗忘。
AI 中文摘要
领域增量学习对于使眼科深度学习模型适应连续的临床领域同时保持诊断专业知识至关重要。现有的领域增量学习方法主要解决由风格变化引起的领域偏移。然而，它们常常忽视现实临床场景（如临床转诊系统）中固有的严重类别不平衡。这些系统中的机构会遇到类别先验的剧烈波动，导致标签分布偏移，这是一种关键的领域偏移形式，会引发严重的灾难性遗忘。为了解决这些挑战，我们提出了ToRe，一种无重放且参数高效的框架，利用冻结的眼科基础模型进行稳健的增量适应。ToRe采用参数隔离策略来解耦特定领域的优化路径，从而有助于缓解由标签分布偏移和风格变化驱动的灾难性遗忘。同时，它引入了令牌自适应递归，该机制跨令牌自适应地分配额外的计算深度，允许简单令牌提前退出递归循环，而将复杂令牌（如与病变相关的令牌）置于更深的递归处理中。该机制增强了少数类别的特征表示，从而在整个领域增量学习过程中支持泛化。在九个异构数据集上的广泛评估表明，ToRe在三个基准测试中的整体性能上始终优于最先进的方法，同时保持接近零的遗忘。这些结果共同支持ToRe在动态和不平衡临床环境中的适用性。代码可在以下网址获取：https URL
英文摘要
Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic expertise. Existing domain incremental learning methods predominantly address the domain shift induced by style variations. However, they often overlook the severe class imbalance inherent in real-world clinical scenarios, such as clinical referral systems. Institutions in these systems encounter drastic fluctuations in class priors, resulting in label distribution shift, a critical form of domain shift that triggers severe catastrophic forgetting. To address these challenges, we propose ToRe, a rehearsal-free and parameter-efficient framework that leverages frozen ophthalmic foundation models for robust incremental adaptation. ToRe employs a parameter isolation strategy to decouple domain-specific optimization paths, thereby helping mitigate catastrophic forgetting driven by both label distribution shift and style variations. Simultaneously, it introduces token-adaptive recursion that adaptively allocates additional computational depth across tokens, allowing simple tokens to exit the recursion loop early while subjecting complex tokens, such as those associated with lesions, to deeper recursive processing. This mechanism enhances the feature representations for minority classes, thereby supporting generalization throughout the domain incremental learning process. Extensive evaluations on nine heterogeneous datasets demonstrate that ToRe consistently outperforms state-of-the-art methods in overall performance across the three benchmarks, while maintaining near-zero forgetting. Together, these results support the applicability of ToRe to dynamic and imbalanced clinical environments. The code is available at https://github.com/Nancyolo/ToRe
Comments14 pages, 8 figures, 10 tables
