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Dimitrios Bounias¹, Michael Baumgartner², Peter Neher³

¹German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany.

Computers in biology and medicine

|November 6, 2025

概括

本研究为乳腺癌检测引入了风险调整的FROC (raFROC),通过考虑损伤风险来改善模型评估. 这一新型指标在医疗物体检测性能分析中增强了临床相关性.

科学领域:

医学成像分析分析医学成像分析
机器学习在医疗保健中的应用
放射学和定量成像技术

背景情况:

目前的乳腺癌检测模型使用自由响应接收器操作特征 (FROC) 进行性能评估.
FROC不考虑错过或检测到的病变的不同临床影响.
需要将临床预后和风险失衡纳入用于医疗物体检测的机器学习模型评估中.

研究的目的:

为评估乳腺癌检测模型提出一种新型风险调整的FROC (raFROC) 度量.
在模型性能评估中更好地反映病变的临床意义.
提高机器学习在医学成像中的临床实用性.

主要方法:

通过调整标准FROC方法,开发了风险调整的FROC (raFROC).
实施了对焦损失 (raFocal) 的风险调整调整,用于模型培训.
在两个独立的乳腺MRI数据集上评估了拟议的方法,其中包括1735名受试者的1535个病变.

主要成果:

拟议的raFROC指标表明了临床潜力和相对于传统评估方法的优势.
使用 raFocal 改善了 nnDetection 模型的 raFROC 结果和患者级性能.
在不影响标准FROC评估的情况下实现了性能改进.

结论:

raFROC为乳腺癌检测和医疗物体检测提供了一个更具临床相关性的评估框架.
将风险调整纳入评估指标和损失函数中,可以提高模型性能和临床适用性.
提出的方法代表了迈向更准确,更具临床意义的医学成像人工智能的重要一步.

关键词:

乳腺癌是什么? 乳腺癌是什么机器学习机器学习医学成像医学成像对象检测检测对象检测对象检测

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