Evidence map›Paper›PMID 35480202›Full record

ArticleRSC advances2021

Rare bioparticle detection

Shaobo Luo, Yuzhi Shi, Lip Ket Chin, Yi Zhang, Bihan Wen, Ying Sun, Binh T T Nguyen, Giovanni Chierchia, Hugues Talbot, Tarik Bourouina and 2 more

Abstract read
In one paragraph

Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Automatic classification ofBioImpacts : BI · 2025
    Article
  3. 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

12 authors.

Shaobo LuoESYCOM, CNRS UMR 9007, Universite Gustave Eiffel, ESIEE Paris Noisy-le-Grand 93162 France tarik.bourouina@esiee.fr.
Yuzhi ShiSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.
Lip Ket ChinSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.ORCID https://orcid.org/0000-0001-9020-7782
Yi ZhangSchool of Mechanical & Aerospace Engineering, Nanyang Technological University 639798 Singapore.
Bihan WenSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.
Ying SunInstitute for Infocomm Research (I2R), Agency for Science, Technology and Research (ASTAR) 138668 Singapore.
Binh T T NguyenSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.
Giovanni ChierchiaESYCOM, CNRS UMR 9007, Universite Gustave Eiffel, ESIEE Paris Noisy-le-Grand 93162 France tarik.bourouina@esiee.fr.
Hugues TalbotCentraleSupelec, Universite Paris-Saclay Saint-Aubin 91190 France.
Tarik BourouinaESYCOM, CNRS UMR 9007, Universite Gustave Eiffel, ESIEE Paris Noisy-le-Grand 93162 France tarik.bourouina@esiee.fr.ORCID https://orcid.org/0000-0003-2342-7149
Xudong JiangSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.
Ai-Qun LiuSchool of Electrical & Electronic Engineering, Nanyang Technological University 639798 Singapore EXDJiang@ntu.edu.sg EAQLiu@ntu.edu.sg.ORCID https://orcid.org/0000-0002-0126-5778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate,

Identifiers

PMID35480202
PMCPMC9032704

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.