论文标题
基于时域辐射传递方程的弥漫性光学断层扫描的深度学习
Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation
论文作者
论文摘要
近红外弥漫性光学断层扫描（DOT）为组织的氧合提供了成像方式。在本文中，我们提出了一种基于时间域辐射转移方程的新型机器学习算法。我们对目标组织的二维模型使用吸收度量的时间曲线，该模型是通过求解时间域辐射转移方程来计算的。应用长期记忆（LSTM）深度学习方法，我们发现我们可以以高精度率指定癌细胞的位置。我们证明目前的算法也可以预测多个或扩展的癌细胞。
Near infrared diffuse optical tomography (DOT) provides an imaging modality for the oxygenation of tissue. In this paper, we propose a novel machine learning algorithm based on time-domain radiative transfer equation. We use temporal profiles of absorption measure for a two-dimensional model of target tissue, which are calculated by solving time-domain radiative transfer equation. Applying a long-short-term memory (LSTM) deep learning method, we find that we can specify positions of cancer cells with high accuracy rates. We demonstrate that the present algorithm can also predict multiple or extended cancer cells.
