让AI模型持续学习不同医院的肺部X光差异，避免遗忘旧知识。
Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis
- 用回放机制动态保存关键数据，防止新旧知识冲突。
- 在5个模拟医院数据集上平均准确率达88.66%，优于传统方法。
- 适合需要长期更新、跨机构部署的医疗影像系统使用。
深度学习模型在胸部X光肺炎检测中已取得高精度，但其在不同临床场景下的泛化能力受限于成像设备、采集协议及机构条件的差异。本文提出一种基于回放的领域增量持续学习方法，实现对跨域变化的持续适应，同时避免灾难性遗忘。该方法结合类感知均衡回放，以在有限内存中保持各类别样本平衡，并引入类感知损失，在训练过程中动态重加权类别不平衡问题。在包含五个模拟领域的肺炎MNIST数据集上进行实验，结果表明，所提方法平均准确率达到88.66%，优于经验回放、微调和联合训练等基线方法。研究验证了该方法在应对临床环境变化时实现鲁棒且一致的肺炎检测的有效性。
原文摘要 · Abstract (English)
Deep learning models achieved high accuracy in pneumonia detection from chest X-rays. However, their generalization across clinical domains remains limited due to variations in imaging devices, acquisition protocols, and institutional conditions. This study introduces a replay-based domain-incremental continual learning designed to enable continual adaptation to cross-domain variations without catastrophic forgetting. The proposed method incorporates a class-aware balanced replay to maintain balanced class representation within a constrained memory and a class-aware loss to dynamically reweight class imbalance during training. Experiments conducted on a domain-shifted PneumoniaMNIST dataset consisting of five simulated domains demonstrate that the proposed method achieves an average accuracy of 88.66%, outperforming Experience Replay, Fine-Tuning, and Joint Training baselines. These findings highlight the efficacy of the proposed approach in achieving robust and consistent pneumonia detection across clinical environment variations.
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