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English(EN) Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening

ECG基础模型在心脏病筛查方面展现潜力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种方法，用于将预训练的心电图（ECG）基础模型改编用于筛查结构性心脏病（SHD）。通过在EchoNext数据集上应用领域内自监督适应和选择性监督微调，这些改编后的模型在检测六种特定的超声心动图衍生异常方面取得了优越的性能。研究强调，这种结合了适应和微调的迁移学习策略是基于ECG的病例发现和超声心动图分诊最有效的方法。 AI

影响 这项研究展示了一种有效的医学基础模型迁移学习策略，有望提高心脏病学诊断效率。

排序理由 该集群包含一篇学术论文，详细介绍了一种改编基础模型的新方法。

在 arXiv cs.LG 阅读 →

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

ECG基础模型在心脏病筛查方面展现潜力

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该集群包含一篇学术论文，详细介绍了一种改编基础模型的新方法。
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Duc N. Do, Minh N. Do, Dang Nguyen, Khanh T. Q. Le, Khoa D. Pham, Hung N. Huynh, Phi Pham-Van-Hoang, Quan K. Huynh, Ramez M. Odat, Perisa Ashar, Ethan Philip Lowder, Minh H. N. Le, Hoang Le, Phat V. H. Nguyen, Quan Le, Jacques Kpodonu, Phat K. Huynh ·

    面向多标签结构性心脏病筛查的领域自适应ECG基础模型微调

    arXiv:2604.23385v1 Announce Type: new Abstract: Transthoracic echocardiography is the reference standard for confirming structural heart disease (SHD), but first-line screening is limited by cost, workflow burden, and specialist availability. We evaluated whether open pretrained …

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