通过领域知识驱动的对比学习来进行开放世界的心电图分类
Shuang Zhou1, Xiao Huang1, Ninghao Liu2
1Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
概括
这项研究引入了开放世界的心电图 (ECG) 分类,以识别已知和未知的心脏病类型. 这种新的方法提高了未见的心电图类的诊断准确性,提高了自动诊断的可靠性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动心电图 (ECG) 分类有助于诊断疾病,但在仅涵盖特定心电图类型的有限培训数据方面存在困难.
- 传统模型无法识别现实数据中存在的未见或未知的心电图类型.
- 这种限制阻碍了自动诊断工具在各种临床场景中的可靠性.
研究的目的:
- 为了应对在开放世界的环境中分类有限的已知的ECG类型和识别未知的ECG类型的挑战.
- 开发一种新的开放世界心电图分类方法,以提高观察到和未观察到的心电图类别的识别.
- 提高自动化心电图诊断系统的稳定性和可靠性.
主要方法:
- 提出了一个定制的方法,将临床知识整合到对比学习中.
- 生成"硬负"样本,以指导可区分的诊断心电图特征的学习.
- 采用多个超球体学习来进行紧的心电图表示和分类.
主要成果:
- 拟议的方法在12个的ECG数据集上与最先进的方法相比显示出更高的性能 (CPSC2018,PTB-XL,格鲁吉亚).
- 在识别未见的ECG类和某些可见的类方面取得了更高的准确性.
- 优于比较方法,突出其在开放世界场景中的有效性.
结论:
- 开放世界心电图分类对于提高自动心电图诊断的可靠性至关重要.
- 拟议的方法有效地解决了分类已知的和未知的ECG类型的挑战.
- 这些发现有助于更强大的和临床上适用的自动化心电图分析工具.
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