Wavefront reconstruction based on deep transfer learning for microscopy

Optics Express
Yuncheng JinKe Si

Abstract

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% hig...Continue Reading

References

Sep 15, 2007·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Martin J Booth
Aug 6, 2009·Optics Express·Julia W EvansJohn S Werner
Aug 31, 2010·Journal of Biomedical Optics·Jae Won ChaPeter T C So
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Jan 16, 2019·Optics Express·Yohei NishizakiEsteban Vera
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Mar 16, 2019·Applied Optics·Zhenxing XuHeping Li
Jun 30, 2019·Optics Express·Yiye ZhangWei Gong

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Citations

Oct 27, 2020·Applied Optics·Kathleen KeeChristopher C Davis

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