乳腺DCE-MRI瘤细分的域转移:在MAMA-MIA数据集上的平衡LoCoCV研究
Munid Alanazi1, Bader Alsharif2
1Business Informatics Department, College of Business, King Khalid University, Abha 61421, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
对于乳腺MRI细分的深度学习模型在新医院由于域移动而出现性能下降. 强大的多中心培训和验证对于可靠的临床部署至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 在动态对比增强MRI (DCE-MRI) 中精确的乳腺瘤细分对于癌症治疗和监测至关重要.
- 深度学习模型通常在应用到来自不同医院的数据时会出现性能下降,原因是硬件,协议和患者群体的变化 (与中心相关的领域转移).
研究的目的:
- 通过使用多中心MAMA-MIA数据集,研究中心相关领域转移对自动乳腺DCE-MRI瘤细分的影响.
- 评估不同交叉验证策略的有效性,以评估模型在各机构的通用性.
主要方法:
- 训练了一种用于初级瘤细分的3D U-Net模型.
- 使用两种设置评估性能:随机患者分组 (分布式) 和均衡的离开一个中心的交叉验证 (LoCoCV) 协议 (分布式外).
- 使用子相似系数 (DSC),95百分点豪斯多夫距离 (HD95),灵敏度和特异性评估细分精度.
主要成果:
- 在混合中心随机分割中,该模型实现了~0.68的平均子和~19.7毫米的HD95,表明训练和测试分布匹配时的表现良好.
- 在平衡的LoCoCV下,性能显著降低,在看不见的中心上平均Dice为~0.45和HD95为~41mm,显示出大量的准确性损失.
- 跨中心验证显示,当模型应用于新机构时,边界误差增加和细分可靠性降低.
结论:
- 在混合中心数据上表现良好的模型可能会在看不见的机构上经历显著的准确性损失.
- 平衡的LoCoCV协议有效地突出了与中心相关的域名转移引起的分布外处罚.
- 强调需要强大的多中心培训策略和明确的跨中心验证,以临床部署乳腺DCE-MRI细分模型.
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