Evidence map›Paper›PMID 42042505›Full record

ArticleJournal of imaging2026

D

Lingge Lai, Weihua Ou, Jianping Gou, Zhonghua Liu

Abstract read
In one paragraph

Article in Journal of imaging, 2026. 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

4 authors.

Lingge LaiSchool of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China.
Weihua OuSchool of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China.
Jianping GouSchool of Computer and Information Sciences, Southwest University, Chongqing 400715, China.ORCID 0000-0003-1413-0693
Zhonghua LiuSchool of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China.ORCID 0000-0001-9816-7471

Funding

High-level Innovative Talents in Guizhou Province GCC[2023]033National Natural Science Foundation of China 62262005Natural Science Research Project of the Department of Education of Guizhou Province QJJ[2024]009;QJJ[2023]011
6 · The paper itself

Abstract

Difference visual question answering (Diff-VQA) aims to answer questions by identifying and reasoning about differences between medical images. Existing methods often rely on simple feature subtraction or fusion to model image differences, while overlooking the asymmetric descriptive requirements of changed and unchanged cases and providing limited task-specific guidance to pretrained language decoders. To address these limitations, we propose D

Indexed as

chest X-raymedical vision question answeringmulti-prompt learning

Identifiers

PMID42042505
PMCPMC13117283

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.