Corpus ID: 215547992

Physics-enhanced machine learning for virtual fluorescence microscopy

@article{Cooke2020PhysicsenhancedML,
  title={Physics-enhanced machine learning for virtual fluorescence microscopy},
  author={Colin L. Cooke and Fanjie Kong and Amey Chaware and Kevin C Zhou and Kanghyun Kim and Rong Xu and D. Michael Ando and Samuel J. Yang and Pavan Chandra Konda and Roarke Horstmeyer},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.04306}
}
This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy. Our results show that by including a model of illumination within the first layers of a deep convolutional neural network, it is possible to learn task-specific LED patterns that substantially improve the ability to infer fluorescence image information from unstained transmission microscopy images. We validated our method on two different experimental setups, with different magnifications… Expand
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