Balancing Bias and Variance in Deep Learning-Based Tumor Microstructural Parameter Mapping

doi:10.1002/mrm.70154

2025-10-22

PRE

Jiaren Zou *

Yue Cao (Yue Cao)

DOI：10.1002/mrm.70154

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摘要

En 中文

时序依赖的扩散MRI能够量化对诊断和预后有用的肿瘤微结构参数。然而，当前模型拟合方法表现出次优的偏差-方差权衡；具体而言，非线性最小二乘拟合（NLLS）表现出低偏差但高方差，而使用均方误差损失（MSE-Net）训练的监督深度学习方法则产生了低方差但高偏差。本研究探讨了这些偏差-方差特性，并提出了一种控制拟合偏差和方差的方法。

Keyword:

deep learning

diffusion MRI

head and neck cancers

model fitting

tissue microstructure

期刊

Magnetic Resonance in Medicine

IF:

论文数:

1.2W

被引数:

3.1W

机构

University of Michigan

学者数:

6.4W

论文数: 5.3W

被引数: 124

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