Evidence map›Paper›PMID 34337394›Full record

ArticleFrontiers in artificial intelligence2021

Lung Cancer Segmentation With Transfer Learning: Usefulness of a Pretrained Model Constructed From an Artificial Dataset Generated Using a Generative Adversarial Network.

Mizuho Nishio, Koji Fujimoto, Hidetoshi Matsuo, Chisako Muramatsu, Ryo Sakamoto, Hiroshi Fujita

Open access · goldAbstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.7field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 44 citations in OpenAlex.

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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 at 5 institutions in 1 country.

Mizuho NishioDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Koji FujimotoDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Hidetoshi MatsuoDepartment of Radiology, Kobe University Hospital, Kobe, Japan.
Chisako MuramatsuFaculty of Data Science, Shiga University, Gifu, Japan.
Ryo SakamotoDepartment of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Hiroshi FujitaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Japan.
Kobe University Hospital · JPGifu University · JPKyoto University · JPKyoto University Hospital · JPShiga University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

computed tomographydeep learninggenerative adversarial network 3lung cancerlung nodulesegmentation

Identifiers

PMID34337394
PMCPMC8322116
OpenAlexW3186852274

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.