Evidence map›Paper›PMID 39732945›Full record

ArticleScientific reports2024

Cross-domain lung opacity detection via adversarial learning and box fusion.

Jun Yao, Zhilin Guo, Xin Zhang, Nan Yan, Qiong Wang, Wei Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

6 authors.

Jun YaoThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Zhilin GuoThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Xin ZhangThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Nan YanThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Qiong WangThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Wei YuThe Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China. yuweits@cumt.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays. Focal loss, GIoU loss, and WBF (weighted boxes fusion) were used in training. We conducted extensive experiments on Chest X-rays from RSNA (Radiological Society of North America) and Vingroup Big Data Institute to verify the performance of cross-domain detection. The results indicate that our method has superior performance. The AP reached 34.30% and 36.55%, while the AR

Indexed as

LungAlgorithmsHumansRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicAdversarial learningBox fusionCross-domainLung opacityX-rays

Identifiers

PMID39732945
PMCPMC11682102

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.