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为MRI前列腺细分和癌症检测优化联合学习配置:模拟研究
Ashkan Moradi1, Fadila Zerka1, Joeran Sander Bosma2
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Olav Kyrres gate 9, 7030 Trondheim, Norway.
Radiology. Artificial intelligence
|July 30, 2025
概括
联合学习 (FL) 显著改善了MRI前列腺细分和癌症检测性能. 与本地模型相比,优化FL配置进一步提高了病变检测准确度.
科学领域:
- 放射学中的人工智能
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 联合学习 (FL) 提供了一种保护隐私的方法,用于在多个机构中开发人工智能模型,而无需共享原始患者数据.
- 使用双参数MRI进行前列腺癌检测和细分对于准确的诊断和治疗计划至关重要.
- 为这些任务开发强大的AI模型需要多样化的数据集和优化的培训策略.
研究的目的:
- 开发和优化一个联合学习 (FL) 框架,以增强双参数MRI前列腺细分和临床显著前列腺癌 (csPCa) 检测.
- 评估优化FL模型的性能与本地客户端模型和基线FL模型相比.
- 为分段和检测任务确定最佳的FL配置 (时代,轮,聚合策略).
主要方法:
- 一项回顾性研究利用了Flower FL框架来训练基于nnU网络的架构,用于从2010年1月到2021年8月的双参数MRI数据.
- 模型开发涉及优化局部时代,联合轮和聚合策略 (FedMedian,FedAdagrad) 进行前列腺细分 (4名客户,1294名患者) 和csPCa检测 (3名客户,1440名患者).
- 使用Dice分数对细分和前列腺成像:癌症人工智能 (PI-CAI) 分数对独立测试集的csPCa检测进行了性能评估,统计学意义通过排列测试确定.
主要成果:
- 优化的FL配置 (1时代,300轮使用FedMedian进行细分;5时代,200轮使用FedAdagrad进行检测) 显著提高了比平均客户端模型的性能.
- 优化的FL模型在前列腺细分 (迪斯分数:0.73 ± 0.06 至 0.88 ± 0.03;P ≤ .01) 和csPCa检测 (PI-CAI分数:0.63 ± 0.07 至 0.74 ± 0.06;P ≤ .01) 中显示了显著的改善.
- 与FL基线模型相比,优化的FL模型显示出优异的病变检测性能 (PI-CAI得分:0.72 ± 0.06至0.74 ± 0.06;P ≤ .01),在细分性能上没有显著差异.
结论:
- 与本地培训方法相比,联合学习提高了MRI前列腺细分和csPCa检测的AI模型的性能和通用性.
- 优化FL框架配置,包括聚合策略和训练参数,进一步提高模型的病变检测能力.
- 这项研究强调了联合学习在推进前列腺癌人工智能驱动诊断工具的潜力,同时保持患者数据隐私.
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