Evidence map›Paper›PMID 36864032›Full record

ReviewLight, science & applications2023

Deep learning-enabled virtual histological staining of biological samples.

Bijie Bai, Xilin Yang, Yuzhu Li, Yijie Zhang, Nir Pillar, Aydogan Ozcan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Light, science & applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
132citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

132 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article

72 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Bijie Bai *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Xilin Yang *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yuzhu LiElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yijie ZhangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Nir PillarElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID http://orcid.org/0000-0003-4979-1440
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.

Identifiers

PMID36864032
PMCPMC9981740

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.