Evidence map›Paper›PMID 38177276›Full record

ArticleNature nanotechnology2024

Multichannel meta-imagers for accelerating machine vision.

Hanyu Zheng, Quan Liu, Ivan I Kravchenko, Xiaomeng Zhang, Yuankai Huo, Jason G Valentine

Abstract read
In one paragraph

Article in Nature nanotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–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

32 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Compound Meta-Optics for Advanced Optical Engineering.Sensors (Basel, Switzerland) · 2026
    Review
  11. Article
  12. Review
  13. Article
  14. Compound meta-optics: there is plenty of room at the top.Nanophotonics (Berlin, Germany) · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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.

Hanyu ZhengDepartment of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.ORCID http://orcid.org/0000-0003-1922-8112
Quan LiuDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Ivan I KravchenkoCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA.ORCID http://orcid.org/0000-0003-4999-5822
Xiaomeng ZhangDepartment of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA.
Yuankai HuoDepartment of Computer Science, Vanderbilt University, Nashville, TN, USA.
Jason G ValentineDepartment of Mechanical Engineering, Vanderbilt University, Nashville, TN, USA. jason.g.valentine@vanderbilt.edu.ORCID http://orcid.org/0000-0001-9943-7170

Funding

AI-empowered 3D Computer Vision and Image-Omics Integration for Digital Kidney HistopathologyR01DK135597 · NIDDK · VANDERBILT UNIVERSITY · PI Yuankai Huo · 2023 to 2026
$2.1M
NIDDK NIH HHS R01 DK135597
6 · The paper itself

Abstract

Rapid developments in machine vision technology have impacted a variety of applications, such as medical devices and autonomous driving systems. These achievements, however, typically necessitate digital neural networks with the downside of heavy computational requirements and consequent high energy consumption. As a result, real-time decision-making is hindered when computational resources are not readily accessible. Here we report a meta-imager designed to work together with a digital back end to offload computationally expensive convolution operations into high-speed, low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positively and negatively valued convolution operations in a single shot. We use our meta-imager for object classification, achieving 98.6% accuracy in handwritten digits and 88.8% accuracy in fashion images. Owing to its compactness, high speed and low power consumption, our approach could find a wide range of applications in artificial intelligence and machine vision applications.

Identifiers

PMID38177276
PMCPMC11031328

What OpenQuestion holds

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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.