Evidence map›Paper›PMID 38686406›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2024

[Reconstruction of elasticity modulus distribution base on semi-supervised neural network].

Xiao Zhang, Bo Peng, Rui Wang, Xingyue Wei, Jianwen Luo

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xiao ZhangSchool of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, P. R. China.
Bo PengSchool of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, P. R. China.
Rui WangDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, P. R. China.
Xingyue WeiDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, P. R. China.
Jianwen LuoDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate reconstruction of tissue elasticity modulus distribution has always been an important challenge in ultrasound elastography. Considering that existing deep learning-based supervised reconstruction methods only use simulated displacement data with random noise in training, which cannot fully provide the complexity and diversity brought by

Indexed as

Elasticity Imaging TechniquesElastic ModulusImage Processing, Computer-AssistedNeural Networks, ComputerPhantoms, ImagingAlgorithmsDeep LearningHumansDeep learningInverse problemReconstruction of elasticity modulus distributionSemi-supervise

Identifiers

PMID38686406
PMCPMC11058507

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.