让AI在医疗设备上持续学习，不丢旧知识还能防误诊
On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis
- 用双阶段缓冲区保持各类数据均衡重放
- 在模拟5种场景下准确率达86.6%，遗忘率仅1.4%
- 适合资源受限的便携式医疗设备部署
深度学习模型在胸部X光片中检测肺炎具有高准确率，但在设备、患者或机构差异导致的分布漂移下性能下降。我们提出PneumoNet，一种面向资源受限场景的点对点肺炎诊断领域增量学习方法。PneumoNet结合轻量级CNN实现设备端预测、双阶段平衡缓冲区进行类别均衡回放，以及动态类别加权损失以纠正训练批次不平衡。在模拟五种真实域变化情景的domain-shifted PneumoniaMNIST数据集上评估，PneumoNet达到86.6%准确率，遗忘率仅为1.4%，且模型更小、运行更快。结果表明，PneumoNet有望实现可适应、隐私保护的诊断AI，在真实世界及疫情应对的医疗环境中直接部署于点对点医疗设备。
原文摘要 · Abstract (English)
Deep learning models detect pneumonia from chest X-rays with high accuracy, but the performance declines under domain shifts caused by differences in devices, patients, or institutions. We present PneumoNet, a domain-incremental learning method for point-of-care pneumonia diagnosis in resource-limited settings. PneumoNet combines a lightweight CNN for on-device prediction, a dual-stage balanced buffer for class-balanced replay, and a dynamic class-weighted loss to correct training-batch imbalances. Evaluated on a domain-shifted PneumoniaMNIST dataset simulating five realistic domain change scenarios, PneumoNet achieves 86.6% accuracy with 1.4% forgetting while being smaller and faster than existing baselines. These results highlight PneumoNet's potential to enable adaptive, privacy-preserving diagnostic AI directly on point-of-care medical devices in real-world and pandemic-ready healthcare.
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