领域增量学习用于疫情 resilient 胸部X光分析
Domain Incremental Learning for Pandemic-Resilient Chest X-Ray Analysis
AI总结：
本文提出了一种基于回放的领域增量持续学习方法，用于在跨领域变化中保持肺炎检测的鲁棒性和一致性，通过类感知平衡回放和类感知损失实现平衡的类表示和动态重加权，实验表明该方法在领域偏移的PneumoniaMNIST数据集上达到88.66%的平均准确率，优于经验回放、微调和联合训练基线。
摘要：
深度学习模型在肺炎检测中实现了高准确性，但其在临床领域中的泛化能力受限于成像设备、获取协议和机构条件的差异。本研究引入了一种基于回放的领域增量持续学习方法，旨在使模型能够持续适应跨领域变化而不发生灾难性遗忘。所提出的方法结合了类感知平衡回放以在受限内存中保持平衡的类表示，以及类感知损失以在训练过程中动态重新加权类不平衡。在包含五个模拟领域的领域偏移PneumoniaMNIST数据集上进行的实验表明，所提出的方法实现了88.66%的平均准确率，优于经验回放、微调和联合训练基线。这些发现突显了所提出方法在跨临床环境变化中实现稳健和一致肺炎检测的有效性。
英文摘要：
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.
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。
