通过患者级潜在特征漂移识别关键样本，缓解医疗影像持续学习中的遗忘问题。
Stable-Drift: A Patient-Aware Latent Drift Replay Method for Stabilizing Representations in Continual Learning
- 以潜在特征漂移衡量样本表示不稳定性，指导重放选择
- 在跨医院新冠CT分类任务中显著降低遗忘率
- 适合需要持续更新但不能丢弃旧知识的医疗AI系统
当深度学习模型在新数据上顺序训练时，往往会突然丧失对先前任务的性能，这种严重缺陷称为灾难性遗忘。该问题极大限制了AI在医学影像领域的部署，因为模型必须不断适应新医院的数据，同时不损害已有的诊断知识。为此，我们提出一种基于潜在漂移引导的重放方法，通过量化样本内部特征表示的变化来识别高不稳定性样本。具体而言，利用潜在漂移（即样本在简单领域自适应后特征表示的变化）作为信号，并在患者层面聚合漂移信息，记忆缓冲区存储每位患者中多层特征变化最大的切片。在使用先进CNN与视觉变压器骨干网络的跨医院新冠CT分类任务上，该方法显著优于直接微调和随机重放。本工作表明，潜在漂移是现实医疗场景中实现鲁棒持续学习的一种实用且可解释的重放信号。
原文摘要 · Abstract (English)
When deep learning models are sequentially trained on new data, they tend to abruptly lose performance on previously learned tasks, a critical failure known as catastrophic forgetting. This challenge severely limits the deployment of AI in medical imaging, where models must continually adapt to data from new hospitals without compromising established diagnostic knowledge. To address this, we introduce a latent drift-guided replay method that identifies and replays samples with high representational instability. Specifically, our method quantifies this instability via latent drift, the change in a sample internal feature representation after naive domain adaptation. To ensure diversity and clinical relevance, we aggregate drift at the patient level, our memory buffer stores the per patient slices exhibiting the greatest multi-layer representation shift. Evaluated on a cross-hospital COVID-19 CT classification task using state-of-the-art CNN and Vision Transformer backbones, our method substantially reduces forgetting compared to naive fine-tuning and random replay. This work highlights latent drift as a practical and interpretable replay signal for advancing robust continual learning in real world medical settings.
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