The application of machine learning in wavefront reconstruction has brought great benefits to real-time, non-invasive, deep tissue imaging in biomedical research. However, due to the diversity and heterogeneity of biological tissues, it is difficult to train the dataset with a unified model. In general, the utilization of some unified models will result in the specific sample falling outside the training set, leading to low accuracy of the machine learning model in some real applications. This paper proposes a sensorless wavefront reconstruction method based on transfer learning to overcome the domain shift introduced by the difference between the training set and the target test set. We build a weights-sharing two-stream convolutional neural network (CNN) framework for the prediction of Zernike coefficient, in which a large number of labeled randomly generated samples serve as the source-domain data and the unlabeled specific samples serve as the target-domain data at the same time. By training on massive labeled simulated data with domain adaptation to unlabeled target-domain data, the network shows better performance on the target tissue samples. Experimental results show that the accuracy of the proposed method is 18.5% higher than that of conventional CNN-based method and the peak intensities of the point spread function (PSF) are more than 20% higher with almost the same training time and processing time. The better compensation performance on target sample could have more advantages when handling complex aberrations, especially the aberrations caused by various histological characteristics, such as refractive index inhomogeneity and biological motion in biological tissues.

中文翻译：

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基于深度传输学习的显微镜波前重建。

机器学习在波前重建中的应用为生物医学研究中的实时，无创，深层组织成像带来了巨大的好处。但是，由于生物组织的多样性和异质性，很难用统一模型训练数据集。通常，某些统一模型的使用将导致特定样本不在训练集中，从而导致在某些实际应用中机器学习模型的准确性较低。提出了一种基于转移学习的无传感器波阵面重构方法，以克服训练集与目标测试集之间的差异所引起的域偏移。我们建立了一个权重共享的两流卷积神经网络（CNN）框架来预测Zernike系数，其中大量标记的随机生成样本用作源域数据，而未标记的特定样本同时用作目标域数据。通过对大量标记的模拟数据进行训练，并具有对未标记的目标域数据的域适应性，该网络在目标组织样本上显示出更好的性能。实验结果表明，在几乎相同的训练时间和处理时间的情况下，该方法的精度比传统的基于CNN的方法高18.5％，并且点扩散函数（PSF）的峰值强度高出20％以上。当处理复杂的像差，尤其是由各种组织学特征引起的像差时，对目标样品更好的补偿性能可能具有更多优势，