🤖 AI Summary
This work addresses the scarcity of annotated data in electrocardiogram (ECG) segmentation by proposing CardioMix, a framework that introduces a cardiac rhythm–guided bidirectional CutMix strategy to enable physiologically consistent data augmentation between labeled and unlabeled samples. By explicitly modeling the temporal patterns of cardiac electrical activity, CardioMix ensures that augmented samples maintain physiological plausibility. Designed as a plug-and-play module, it seamlessly integrates with prevailing semi-supervised segmentation algorithms. Evaluated on the SemiSegECG multi-dataset benchmark, CardioMix consistently outperforms existing CutMix variants across varying label proportions, achieving robust and state-of-the-art segmentation performance.
📝 Abstract
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.