论文标题
用深度学习方法补偿光声成像中的可见性人工制品提供预测不确定性
Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties
论文作者
论文摘要
传统的光声成像可能会受到超声传感器的有限视图和带宽。提出了一种深度学习方法来解决这些问题，并在模拟和对叶骨架的多尺度模型实验中进行了证明。我们采用了一种实验方法来建立培训和测试集，并使用样本的照片作为地面真相图像。与传统方法相比，神经网络产生的重建表现出大大提高的图像质量。此外，这项工作旨在量化神经网络预测的可靠性。为了实现这一目标，使用辍学的蒙特卡罗程序来估计每个预测图片的置信度。最后，我们解决了将转移学习与模拟数据一起使用以大大限制实验数据集的大小的可能性。
Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental approach to build the training and the test sets using photographs of the samples as ground truth images. Reconstructions produced by the neural network show a greatly improved image quality as compared to conventional approaches. In addition, this work aimed at quantifying the reliability of the neural network predictions. To achieve this, the dropout Monte-Carlo procedure is applied to estimate a pixel-wise degree of confidence on each predicted picture. Last, we address the possibility to use transfer learning with simulated data in order to drastically limit the size of the experimental dataset.
