PhaseStain: Deep Learning-based Histological Staining of Quantitative Phase Images

  • Yair Rivenson
  • , Tairan Liu
  • , Zhensong Wei
  • , Kevin De Haan
  • , Yibo Zhan
  • , Aydogan Ozcan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

We demonstrate a digital staining framework that transforms quantitative phase images of label-free tissue sections to match the brightfield microscopy images of the same sections, after histological staining. Inference of multiple tissue-stain combinations is demonstrated.

Original languageEnglish
Title of host publication2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781943580576
DOIs
StatePublished - 1 May 2019
Externally publishedYes
Event2019 Conference on Lasers and Electro-Optics, CLEO 2019 - San Jose, United States
Duration: 5 May 201910 May 2019

Publication series

Name2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings

Conference

Conference2019 Conference on Lasers and Electro-Optics, CLEO 2019
Country/TerritoryUnited States
CitySan Jose
Period5/05/1910/05/19

ASJC Scopus subject areas

  • Spectroscopy
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality
  • Management, Monitoring, Policy and Law
  • Electronic, Optical and Magnetic Materials
  • Radiology Nuclear Medicine and imaging
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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