Stable-Drift: 一种患者感知的潜在漂移重放方法，用于持续学习中稳定表示
Stable-Drift: A Patient-Aware Latent Drift Replay Method for Stabilizing Representations in Continual Learning
AI总结：
Stable-Drift方法通过患者感知的潜在漂移重放，有效减少持续学习中的灾难性遗忘，提升医学影像任务的鲁棒性。
摘要：
当深度学习模型依次在新数据上训练时，它们往往在先前学习的任务上突然失去性能，这种关键失败被称为灾难性遗忘。这一挑战严重限制了人工智能在医学影像中的部署，因为模型必须持续适应新医院的数据而不损害已建立的诊断知识。为此，我们引入了一种潜在漂移引导的重放方法，该方法识别并重放具有高表示不稳定性的样本。具体而言，我们的方法通过潜在漂移来量化这种不稳定，即在进行简单域适应后，样本内部特征表示的变化。为了确保多样性和临床相关性，我们按患者级别聚合漂移，我们的记忆缓冲区存储表现出最大多层表示变化的每个患者的切片。在使用最先进的CNN和视觉Transformer主干进行跨医院新冠肺炎CT分类任务上的评估中，我们的方法相比简单微调和随机重放显著减少了遗忘。本工作强调潜在漂移作为实用且可解释的重放信号，以推动在现实世界医学环境中的稳健持续学习发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
