Unsupervised content-preserving transformation for optical microscopy

HIGHLIGHTS

  • who: Xinyang Li from the (UNIVERSITY) have published the article: Unsupervised content-preserving transformation for optical microscopy, in the Journal: (JOURNAL)
  • what: The authors propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy even in some cases in which supervised models cannot be applied. Some low-SNR images that had never previously been seen by the network were then used to evaluate the model , and the results are shown in Fig 3a.
  • how: For the autofluorescence imaging of label-free TMAs the authors used the fluorescence imaging . . .

     

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