面向领域偏移鲁棒性的多窗口胸部计算机断层扫描隐私感知持续自监督学习
Privacy-Aware Continual Self-Supervised Learning on Multi-Window Chest Computed Tomography for Domain-Shift Robustness
AI总结：
提出一种隐私感知的持续自监督学习框架，通过潜在重放机制和特征蒸馏技术缓解多窗口胸部CT图像的领域偏移与灾难性遗忘，提升模型鲁棒性。
摘要：
我们提出了一种新颖的持续自监督学习（CSSL）框架，用于同时从多窗口获取的胸部计算机断层扫描（CT）图像中学习多样特征并确保数据隐私。在医学图像诊断中实现鲁棒且高度泛化的模型具有挑战性，主要原因是缺乏大规模准确标注的数据集以及动态医疗环境中固有的领域偏移等问题。具体而言，在胸部CT中，这些领域偏移通常源于为不同临床目的优化的窗口设置差异。以往的CSSL框架常通过重用过去数据来缓解领域偏移，但由于隐私限制，这通常是不切实际的方法。我们的方法通过对未标注图像进行持续预训练，有效捕捉了不同训练阶段中先前学习知识与新信息之间的关系，从而解决了这些挑战。具体而言，通过将基于潜在重放的机制引入CSSL，我们的方法在持续预训练期间缓解了由领域偏移导致的灾难性遗忘，同时确保了数据隐私。此外，我们引入了一种特征蒸馏技术，该技术集成了基于Wasserstein距离的知识蒸馏（WKD）和批量知识集成（BKE），增强了模型学习有意义的、领域偏移鲁棒表示的能力。最后，我们使用在两种不同窗口设置下获取的胸部CT图像验证了该方法，证明其性能优于其他方法。
英文摘要：
We propose a novel continual self-supervised learning (CSSL) framework for simultaneously learning diverse features from multi-window-obtained chest computed tomography (CT) images and ensuring data privacy. Achieving a robust and highly generalizable model in medical image diagnosis is challenging, mainly because of issues, such as the scarcity of large-scale, accurately annotated datasets and domain shifts inherent to dynamic healthcare environments. Specifically, in chest CT, these domain shifts often arise from differences in window settings, which are optimized for distinct clinical purposes. Previous CSSL frameworks often mitigated domain shift by reusing past data, a typically impractical approach owing to privacy constraints. Our approach addresses these challenges by effectively capturing the relationship between previously learned knowledge and new information across different training stages through continual pretraining on unlabeled images. Specifically, by incorporating a latent replay-based mechanism into CSSL, our method mitigates catastrophic forgetting due to domain shifts during continual pretraining while ensuring data privacy. Additionally, we introduce a feature distillation technique that integrates Wasserstein distance-based knowledge distillation (WKD) and batch-knowledge ensemble (BKE), enhancing the ability of the model to learn meaningful, domain-shift-robust representations. Finally, we validate our approach using chest CT images obtained across two different window settings, demonstrating superior performance compared with other approaches.
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。
