PAGE：面向智能医疗的过去无关生成重放域增量适应
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare
AI总结：
PAGE是一种面向智能医疗的域增量适应策略，通过过去无关的生成重放实现知识保留，并集成扩展归纳共形预测提供统计保证，在三个疾病数据集上达到先进性能，并减少75%临床工作量。
摘要：
我们提出了PAGE，一种面向智能医疗的过去无关生成重放域增量适应策略。PAGE无需保留先前域的任何数据或信息即可实现生成重放。当适应新域时，它利用来自新分布的真实数据和当前模型来生成保留先前域所学知识的合成数据。通过在训练期间将合成数据与新的真实数据一起重放，PAGE在域适应和知识保留之间取得了良好的平衡。此外，我们将扩展归纳共形预测（EICP）方法纳入PAGE，为每个检测结果产生置信度和可信度值。这使得预测可解释，并为智能医疗应用中的疾病检测提供统计保证。我们使用从商业可用的WMS收集的三个不同疾病数据集，证明了PAGE在域增量疾病检测中的有效性。PAGE在可扩展性、数据隐私和可行性方面表现出色，达到了与最先进方法高度竞争的性能。此外，借助EICP，PAGE可以使临床工作量减少高达75%。
英文摘要：
We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved data or information from prior domains. When adapting to a new domain, it exploits real data from the new distribution and the current model to generate synthetic data that retain the learned knowledge of previous domains. By replaying the synthetic data with the new real data during training, PAGE achieves a good balance between domain adaptation and knowledge retention. In addition, we incorporate an extended inductive conformal prediction (EICP) method into PAGE to produce a confidence score and a credibility value for each detection result. This makes the predictions interpretable and provides statistical guarantees for disease detection in smart healthcare applications. We demonstrate PAGE's effectiveness in domain-incremental disease detection with three distinct disease datasets collected from commercially available WMSs. PAGE achieves highly competitive performance against state-of-the-art with superior scalability, data privacy, and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical workload with the help of EICP.
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。
