IncepSE：在心电图分析中结合Squeeze and Excitation机制发挥InceptionTime性能
IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis
AI总结：
针对心电图分析中数据集不平衡与梯度损坏问题，提出融合InceptionTime与通道注意力机制的IncepSE网络及稳定训练方案，在多数任务上超越现有技术，“全部”任务AUROC提升0.013。
摘要：
本研究聚焦于类Inception架构在心电图（ECG）领域的改进潜力。为此，我们提出了IncepSE，这是一种新型网络，其特点是通过策略性的架构融合，同时发挥InceptionTime和通道注意力机制的优势。此外，我们提出了一种采用稳定化技术的训练方案，旨在应对PTB-XL数据集严重不平衡以及梯度损坏的艰巨挑战。通过这种方式，我们以监督学习的方式在大多数任务上为深度学习模型树立了新的高度。我们的模型相较于该领域的其他现有技术，始终大幅超越InceptionTime，在“全部”任务中AUROC得分提升了0.013，同时还缓解了训练过程中固有的数据集波动问题。
英文摘要：
Our study focuses on the potential for modifications of Inception-like architecture within the electrocardiogram (ECG) domain. To this end, we introduce IncepSE, a novel network characterized by strategic architectural incorporation that leverages the strengths of both InceptionTime and channel attention mechanisms. Furthermore, we propose a training setup that employs stabilization techniques that are aimed at tackling the formidable challenges of severe imbalance dataset PTB-XL and gradient corruption. By this means, we manage to set a new height for deep learning model in a supervised learning manner across the majority of tasks. Our model consistently surpasses InceptionTime by substantial margins compared to other state-of-the-arts in this domain, noticeably 0.013 AUROC score improvement in the "all" task, while also mitigating the inherent dataset fluctuations during training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
