对医疗保健应用的基于联合学习的模型进行全面的审查
Shagun Sharma1, Kalpna Guleria1
1Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura 140401, Punjab, India.
Artificial intelligence in medicine
|December 2, 2023
概括
联合学习通过对分散数据进行培训,使得保护隐私的疾病检测模型成为可能. 本综述分析了联合学习架构和改善医疗预测的方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 早期发现疾病对于有效治疗和预防并发症至关重要.
- 深度学习模型提供了准确的疾病预测,但面临着数据隐私和有限数据集的挑战.
- 联合学习通过培训模型来解决隐私问题,而不会集中敏感的患者数据.
结论:
- 联合学习为推进医疗保健中人工智能驱动的疾病检测提供了一个有希望的方法.
- 该审查确定了成功开发联合学习模式的关键组件和方法.
- 突出了未来的研究方向,解决了医疗应用联合学习的开放挑战.
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