Predicting immunofluorescence images from reflectance microscopy via deep learning

Shiyi Cheng, Sipei Fu, Yumi Mun Kim, Ji Yi, Lei Tian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To circumvent the limitations of immunofluorescence microscopy, we propose a deep learning approach for characterizing morphological information contained in reflectance microscopy with high specificity and enable digital multiplexing.

Original languageEnglish (US)
Title of host publicationMicroscopy Histopathology and Analytics, Microscopy 2020
PublisherOSA - The Optical Society
ISBN (Electronic)9781557528209
DOIs
StatePublished - 2020
Externally publishedYes
EventMicroscopy Histopathology and Analytics, Microscopy 2020 - Washington, United States
Duration: Apr 20 2020Apr 23 2020

Publication series

NameOptics InfoBase Conference Papers
VolumePart F177-Microscopy-2020

Conference

ConferenceMicroscopy Histopathology and Analytics, Microscopy 2020
CountryUnited States
City Washington
Period4/20/204/23/20

ASJC Scopus subject areas

  • Mechanics of Materials
  • Electronic, Optical and Magnetic Materials

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