M

Zhigao Zeng, Jiale Xiao, Shengqiu Yi, Qiang Liu, Yanhui Zhu

Abstract read

In one paragraph

Article in Journal of imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

GHMS-CycleGAN: Graph-Based Hierarchical Multi-stain CycleGAN for Stain Normalization and Classification in Digital Pathology.Journal of imaging informatics in medicine · 2026
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.

Zhigao ZengSchool of Computer and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.

Jiale XiaoSchool of Computer and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.ORCID 0009-0002-0406-1233

Shengqiu YiSchool of Computer and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.

Qiang LiuSchool of Computer and Artificial Intelligence, Hunan University of Technology, Zhuzhou 412007, China.ORCID 0000-0001-8701-8737

Yanhui ZhuSchool of Rail Transit, Hunan University of Technology, Zhuzhou 412007, China.

Funding

National Natural Science Foundation of Hunan Province 2025JJ70071;2025JJ70028;2025JJ81178;2024JJ9550Scientific Research Project of the Education Department of Hunan Province 24A0401

6 · The paper itself

Abstract

Medical image segmentation presents substantial challenges arising from the diverse scales and morphological complexities of target anatomical structures. Although existing Transformer-based models excel at capturing global dependencies, they encounter critical bottlenecks in multi-scale feature representation, spatial relationship modeling, and cross-layer feature fusion. To address these limitations, we propose the M

Indexed as

attention mechanismmedical image segmentationmulti-scaletransunet

Identifiers

PMID41590900

DOI10.3390/jimaging12010015

PMCPMC12843401

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.