Neural network generation for estimation of tissue optical properties
Neural network generation for estimation of tissue optical properties
复制标题
用于估计组织光学特性的神经网络生成
DOI：
10.1117/12.2546068
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Linz, Norbert
中科院分区：
文献类型：
--
作者：
Gil, Eddie M.;Hokr, Brett H.;Bixler, Joel N.;Ibey, Bennett L.;Linz, Norbert
蒙特卡罗模拟 (MCS) 允许在已知几何和光学特性的情况下估计光子在介质中的传播。先前的研究表明，这个问题的逆过程也可以得到解决，即使用光子分布训练的神经网络来估计折射率、散射和吸收系数。为了扩展这项工作，使用与时间相关的 MCS 来生成光子通过各种介质传播的数据集。这些模拟被视为及时的二维图像堆栈，并用于训练卷积网络来估计组织参数。为了找到驱动此任务网络性能的潜在特征，随机生成网络。然后训练生成的网络。使用 4 倍交叉验证对网络进行验证。表现一致的前 10 名网络通常强调卷积链和以最大池化结尾的卷积链。
Monte Carlo Simulations (MCSs) allow for the estimation of photon propagation through media given knowledge of the geometry and optical properties. Previous research has demonstrated that the inverse of this problem may be solved as well, where neural networks trained on photon distributions can be used to estimate refractive index, scattering and absorption coefficients. To extend this work, time-dependent MCSs are used to generate data sets of photon propagation through various media. These simulations were treated as stacks of 2D images in time and used to train convolutional networks to estimate tissue parameters. To find potential features that drive network performance on this task, networks were randomly generated. Generated networks were then trained. The networks were validated using 4-fold cross validation. The consistently performing top 10 networks typically had an emphasis on convolutional chains and convolutional chains ending in max pooling.
DOI：
--
发表时间：
2016-11
期刊：
ArXiv
影响因子：
--
作者：
Barret Zoph;Quoc V. Le
通讯作者：
Barret Zoph;Quoc V. Le
影响因子：
7
作者：
Hokr, Brett H.;Yakovlev, Vladislav V.;Scully, Marlan O.
通讯作者：
Scully, Marlan O.
使用蒙特卡罗方法研究 1064nm 掺钕钇铝石榴石 (Nd:YAG) 激光器的激光与组织相互作用。
影响因子：
1.5
作者：
A. Majdabadi;M. Abazari
通讯作者：
M. Abazari