Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
For researchers in cardiovascular diagnostics, this work addresses the scarcity of annotated ECG data by enhancing semi-supervised segmentation performance, though it is an incremental improvement over existing CutMix strategies.
CardioMix introduces a bidirectional CutMix strategy guided by cardiac patterns for semi-supervised ECG segmentation, improving delineation accuracy by enriching labeled data with realistic variations and applying stronger supervision to unlabeled data. It consistently outperforms existing CutMix-based methods on the SemiSegECG benchmark across diverse datasets and labeled ratios.
Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However, the scarcity of annotated data poses a significant challenge for training deep learning models. Conventional semi-supervised semantic segmentation (SemiSeg) methods primarily focus on consistency from unlabeled data, underutilizing the information exchange possible between labeled and unlabeled sets. To address this, we introduce CardioMix, a framework built on a bidirectional CutMix strategy guided by cardiac patterns for ECG segmentation. This approach enriches the labeled set with realistic variations from unlabeled data while simultaneously applying stronger supervisory signals to the unlabeled set, as the cardiac pattern-guided mixing ensures all augmented samples remain physiologically meaningful. Our framework is designed as a plug-and-play module, demonstrating high compatibility with various SemiSeg algorithms. Extensive experiments on SemiSegECG, a public multi-dataset benchmark for ECG delineation, demonstrate that CardioMix consistently outperforms existing CutMix-based fusion strategies across diverse datasets and labeled ratios as a plug-and-play module compatible with various SemiSeg algorithms.