为了实现医疗预测模型的实际联合学习和评估
Andrei Kazlouski1, Ileana Montoya Perez1, Faiza Noor1
1Department of Computing, University of Turku, Turku, Finland.
International journal of medical informatics
|July 24, 2025
概括
联合学习 (FL) 的好处前列腺癌诊断预测不一致. 它在改善患者护理方面的有效性在很大程度上取决于可用的本地数据的数量,更大的数据集有时没有优势,甚至性能降低.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 保护隐私的技术 保护隐私的技术
背景情况:
- 联合学习 (FL) 允许协作AI模型培训,同时保护敏感的患者数据.
- 医疗保健面临着对集中数据共享的隐私和监管障碍,这使得FL成为一个有希望的替代方案.
- 在疾病检测方面,FL已经证明了其潜力,与集中系统的性能相匹配,但实际应用仍在发展中.
研究的目的:
- 评估联合学习对预测需要前列腺癌活检的实际有效性.
- 引入和评估一种新的联合学习评估策略,即Leave-Silo-Out (LSO).
- 将联合学习模型与本地训练的模型进行比较,重点关注本地患者诊断改进.
主要方法:
- 利用来自10个国家的14个公共前列腺癌数据集进行评估.
- 提出并对Leave-Silo-Out (LSO) 策略进行基准评估,以衡量联合学习绩效与非贡献 (自由驾驶) 相比.
- 研究了多医院联合学习模型与单一机构本地模型的性能.
主要成果:
- 联合学习福利取决于本地注释数据的数量.
- 只有极少数据的医院显示,与自由骑行相比,FL的益处是微不足道的.
- 适度的数据集可能会看到FL的改进,而广泛的数据集通常不会产生任何优势,甚至会降低与本地培训相比的性能.
结论:
- 联合学习为医疗AI的数据稀缺环境提供了潜力.
- 在医疗保健中FL的实际实用性高度取决于具体环境,受数据量和任务需求的影响.
- 需要进一步的研究,以优化FL的实施,以适应不同的临床环境.
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