Evidence map›Paper›PMID 35523553›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2022

[A three dimensional convolutional neural network pulmonary nodule detection algorithm based on the multi-scale attention mechanism].

Yudu Zhao, Zhenwei Peng, Jun Ma, Hao Xia, Honglin Wan

Open access · greenAbstract read
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. [Detection of neurofibroma combining radiomics and ensemble learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Article
  2. 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

5 authors at 1 institution in 1 country.

Yudu ZhaoKey Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
Zhenwei PengKey Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
Jun MaKey Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
Hao XiaKey Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
Honglin WanKey Laboratory of Medical Physical Image Processing Technology, School of Physics and Electronic Science, Shandong Normal University, Jinan 250358, P. R. China.
Shandong Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.

Indexed as

Lung NeoplasmsRadiographic Image Interpretation, Computer-AssistedAlgorithmsHumansNeural Networks, ComputerTomography, X-Ray ComputedAttention mechanismMulti-scale feature extractionPulmonary nodule detectionThree dimensional convolutional neural network

Identifiers

PMID35523553
PMCPMC9927347
OpenAlexW4229336121

What OpenQuestion holds

Textmetadata
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.