Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification

• URL: http://arxiv.org/abs/2503.15578v1
• Date: Wed, 19 Mar 2025 13:22:42 GMT
• Title: Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
• Authors: Jiexia Ye, Weiqi Zhang, Ziyue Li, Jia Li, Fugee Tsung,
• Abstract summary: We introduce Sparseformer, a transformer specifically designed for MedTS classification.<n>We propose a sparse token-based dual-attention mechanism that enables global modeling and token compression.<n>Our model outperforms 12 baselines across seven medical datasets under supervised learning.
• Score: 25.47662257105448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical time series (MedTS) classification is crucial for improved diagnosis in healthcare, and yet it is challenging due to the varying granularity of patterns, intricate inter-channel correlation, information redundancy, and label scarcity. While existing transformer-based models have shown promise in time series analysis, they mainly focus on forecasting and fail to fully exploit the distinctive characteristics of MedTS data. In this paper, we introduce Sparseformer, a transformer specifically designed for MedTS classification. We propose a sparse token-based dual-attention mechanism that enables global modeling and token compression, allowing dynamic focus on the most informative tokens while distilling redundant features. This mechanism is then applied to the multi-granularity, cross-channel encoding of medical signals, capturing intra- and inter-granularity correlations and inter-channel connections. The sparsification design allows our model to handle heterogeneous inputs of varying lengths and channels directly. Further, we introduce an adaptive label encoder to address label space misalignment across datasets, equipping our model with cross-dataset transferability to alleviate the medical label scarcity issue. Our model outperforms 12 baselines across seven medical datasets under supervised learning. In the few-shot learning experiments, our model also achieves superior average results. In addition, the in-domain and cross-domain experiments among three diagnostic scenarios demonstrate our model's zero-shot learning capability. Collectively, these findings underscore the robustness and transferability of our model in various medical applications.

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