English(EN) SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification

新框架通过处理分布外数据改进心电图分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-09 04:00

研究人员开发了SafeECGMatch，一种新颖的用于心电图（ECG）分类的半监督学习框架。该方法通过有效处理可能包含分布外异常的未标记数据，解决了临床环境中标记数据有限的挑战。SafeECGMatch采用双分支架构提取时频表示，并结合自适应校准技术，以确保可靠的分布外拒绝和准确的伪标签。 AI

影响通过提高AI模型处理未见数据的能力，增强了其在医学诊断中的可靠性。

排序理由这是一篇详细介绍心电图分类新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Hongkyu Koh, Ikbeom Jang · 2026-06-09 04:00

SafeECGMatch: 开放集心电图分类的感知校准联合时频空间半监督学习

arXiv:2606.08037v1 Announce Type: cross Abstract: Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs. In clinical settings, however, unlabeled pools freq…