用视觉模型提升医疗时间序列分类，减少个体差异影响。
TS-P$^2$CL: Plug-and-Play Dual Contrastive Learning for Vision-Guided Medical Time Series Classification
- 将生理信号转为伪图像，借力预训练视觉模型
- 双对比学习让时间特征与视觉语义对齐，提升泛化能力
- 在6个数据集上优于14种方法，适合跨病人分析
医疗时间序列（MedTS）分类对智能医疗至关重要，但受个体间差异大导致的跨个体生成能力差限制。尽管架构创新和迁移学习有所进展，现有方法仍受限于模态特异性归纳偏置，难以学习通用不变表征。为此，我们提出TS-P²CL，一种即插即用框架，利用预训练视觉模型的通用模式识别能力。通过将一维生理信号转换为二维伪图像，建立通向视觉领域的桥梁，隐式获取自然图像中学习到的丰富语义先验。在此统一空间中，采用双对比学习策略：模内一致性确保时序连贯性，模间对齐使时间序列动态与视觉语义对齐，从而缓解个体特异性偏差，学习鲁棒的领域不变特征。在六个MedTS数据集上的大量实验表明，TS-P²CL在依赖个体和不依赖个体设置下均持续优于14种方法。
原文摘要 · Abstract (English)
Medical time series (MedTS) classification is pivotal for intelligent healthcare, yet its efficacy is severely limited by poor cross-subject generation due to the profound cross-individual heterogeneity. Despite advances in architectural innovations and transfer learning techniques, current methods remain constrained by modality-specific inductive biases that limit their ability to learn universally invariant representations. To overcome this, we propose TS-P$^2$CL, a novel plug-and-play framework that leverages the universal pattern recognition capabilities of pre-trained vision models. We introduce a vision-guided paradigm that transforms 1D physiological signals into 2D pseudo-images, establishing a bridge to the visual domain. This transformation enables implicit access to rich semantic priors learned from natural images. Within this unified space, we employ a dual-contrastive learning strategy: intra-modal consistency enforces temporal coherence, while cross-modal alignment aligns time-series dynamics with visual semantics, thereby mitigating individual-specific biases and learning robust, domain-invariant features. Extensive experiments on six MedTS datasets demonstrate that TS-P$^2$CL consistently outperforms fourteen methods in both subject-dependent and subject-independent settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。