Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images.

Lin-Na Zhao, Jian-Qiang Li, Wen-Xiu Cheng, Su-Qin Liu, Zheng-Kai Gao, Xi Xu, Cai-Hua Ye, Huan-Ling You

Abstract read

In one paragraph

Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images.Life (Basel, Switzerland) · 2023
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

8 authors.

Lin-Na ZhaoFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Jian-Qiang LiFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Wen-Xiu ChengFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Su-Qin LiuFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Zheng-Kai GaoFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Xi XuFaculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Cai-Hua YeBeijing Meteorological Service Center, Beijing 100089, China.

Huan-Ling YouBeijing Meteorological Service Center, Beijing 100089, China.

Funding

Beijing Municipal Science and Technology Project Z191100009119013

6 · The paper itself

Abstract

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1)

Indexed as

deep learningpollen localization and classificationpollinosis preventionprogressive learningwhole slide images

Identifiers

PMID36552349

DOI10.3390/biology11121841

PMCPMC9775008

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.