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基于卷积神经网络的模型观察者的概括性在乳腺全合成中,跨越体积腺体分数和信号大小

Hanjoo Jang¹, Jongduk Baek^1,2

¹Department of Artificial Intelligence, College of Computing, Yonsei University, Yonsei, South Korea.

Medical physics

|February 28, 2025

概括

卷积神经网络 (CNN) 模型观察者在乳腺图莫合成中显示出更好的检测,但概括性取决于训练数据. 对不同信号大小的培训提高了临床应用的稳定性.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

之前的一项研究提出了基于CNN的模型观察者用于乳腺图莫合成检测任务.
在统计上已知的信号 (SKS) 和统计上已知的背景 (BKS) 任务中接近理想的观察员表现的证明潜力.
需要进一步的研究来扩大适用性,并在各种成像场景中验证稳定性.

研究的目的:

探索基于CNN的模型观察者在乳腺图莫合成中检测SKS和BKS的概括性.
研究不同体积腺素分数 (VGFs) 和信号大小对检测性能的影响.
提供对网络优化的见解,以实现一致和强大的检测性能.

主要方法:

评估了五个具有不同理论接收场 (TRF) 大小的网络架构.
在不同的培训/测试方案下,基于CNN的模型观察员与Hotelling观察员 (HO) 的表现进行了比较.
使用像素级梯度激活映射 (pGrad-CAM) 来探索有效受体场 (ERF) 大小.

主要成果:

网络优先考虑区分类似的信号大小而不是噪声强度.
CNN观察员的表现优于HO,除非在不同大小的信号上进行训练/测试.
与混合VGF数据集相比,对两种信号大小的数据集的训练提高了概括性.
TRF大小匹配信号大小没有提高性能;ERF大小显示相关性.

结论:

在类似信号大小上训练的网络表现最佳,但对不同大小的泛化很差.
对不同信号大小的训练提高了概括性.
基于任务的探索对于设计医疗成像的强大和可泛化的CNN模型观察器至关重要.

关键词:

乳腺图莫合成系统卷积神经网络是一种卷积神经网络.可以概括的概括性.模型观察者模型观察者接收场是一个接收场.在统计学上已知的信号.

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