医学成像中的通用化联合学习:对乳腺造影数据的现实世界案例研究
Ioannis N Tzortzis1, Alberto Gutierrez-Torre2, Stavros Sykiotis1
1School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Heroon Polytechneiou 9, Athens, 15773, Attica, Greece.
Computational and structural biotechnology journal
|April 11, 2025
概括
用于乳腺癌分类的联合学习通过将数据保持在本地,提高了隐私. 一个新的数据协调框架实现了35%的更好的性能,与传统方法相匹配.
科学领域:
- 医疗人工智能 医疗人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 联合学习 (FL) 在医疗应用中越来越多地用于增强数据隐私.
- 将经典的人工智能实验直接应用于FL是具有挑战性的.
- 乳腺癌的分类需要强大的私人AI模型.
研究的目的:
- 为乳腺癌分类适应和评估联合学习.
- 确保经典和联合模式培训之间的等价性.
- 为现实世界医疗数据引入一个强大的FL框架.
主要方法:
- 经典人工智能模型训练与联合变体的比较.
- 引入一个乳房区域检测工具,用于在佛罗里达州统一数据.
- 开发一个端到端的FL框架,用于多医院的部署.
主要成果:
- 拟议的FL框架在一家医院表现出显著的绩效改善.
- 该框架取得的结果与经典的集中式方法相美.
- 干预措施使模型性能提高了约35%.
结论:
- 联合学习可以有效地适应乳腺癌分类,并进行适当的预处理.
- 乳房区域检测工具通过数据协调来提高FL的稳定性.
- 开发的FL框架为私人高性能医疗AI提供了可行的解决方案.
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