Abstract
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.
Abstract (translated)
医学时间序列（MedTS）分类在智能医疗领域至关重要，但其效果因个体间显著的异质性导致跨主体泛化能力差而受到严重限制。尽管在架构创新和迁移学习技术方面取得了进展，目前的方法仍然受制于模式特定的归纳偏差，这限制了它们学习普遍不变特征的能力。为解决这一问题，我们提出了TS-P$^2$CL，这是一种新颖的即插即用框架，利用预训练视觉模型的通用模式识别能力。我们引入了一种以视觉为导向的范式，将1D生理信号转换成2D伪图像，在此基础上建立了一个通往视觉领域的桥梁。这种转换使隐性访问从自然图像中学习到的丰富语义先验成为可能。在这个统一的空间内，我们采用了双对比学习策略：模式内部一致性强制时间连贯性，而跨模态对齐将时间序列动态与视觉语义对齐，从而减轻个体特异性偏差，并学习稳健、域不变的特征。在六个MedTS数据集上的广泛实验表明，在依赖于主体和不依赖于主体的情况下，TS-P$^2$CL方法始终优于其他十四种方法。
URL
https://arxiv.org/abs/2509.17802
