Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification

**A simple model with good features beats a complex model with poor features.** Deep learning in healthcare has increasingly embraced a model-centric paradigm, and medical time series is no exception, with increasingly complex architectures designed to capture richer temporal, cross-channel, and multi-scale dependencies. We argue that this paradigm may overlook a more fundamental bottleneck: the quality and discriminability of the input signals themselves. We therefore shift the focus from designing increasingly complex classifiers toward improving data representations before classification. To this end, we propose CIF, a simple, plug-and-play, and model-agnostic framework for enhancing medical time series representations. CIF can be seamlessly integrated with existing classification architectures without modifying their model designs. Extensive experiments across diverse medical time series datasets and representative classification architectures demonstrate that CIF consistently and substantially improves classification performance, providing strong evidence for the effectiveness of a data-centric alternative. **We hope this work encourages the community to reconsider the core of medical time series classification: should progress be driven primarily by data-centric strategies, model-centric design, or both？**