Evidence map›Paper›PMID 41940578›Full record

ArticleScience progress2026

MSA

Xiao Qin, Chao Deng, Xiaosen Li, Zhengyou Qin, Yuanxu Gong

Abstract read
In one paragraph

Article in Science progress, 2026. 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.

  1. 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.

Xiao QinSchool of Artificial Intelligence, Nanning Normal University, Nanning, People's Republic of China.
Chao DengSchool of Artificial Intelligence, Nanning Normal University, Nanning, People's Republic of China.ORCID 0009-0009-8520-1656
Xiaosen LiSchool of Computer Science, Sichuan University, Chengdu, People's Republic of China.
Zhengyou QinSchool of Artificial Intelligence, Nanning Normal University, Nanning, People's Republic of China.
Yuanxu GongSchool of Artificial Intelligence, Nanning Normal University, Nanning, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The nnU-Net framework effectively automates hyperparameter selection; however, its fixed internal configurations-notably convolution kernel sizes-restrict its flexibility. This limitation is pronounced in 3D medical imaging, where anatomical structures undergo continuous spatial evolution along the Z-axis. In this study, we introduce a self-adaptive convolution module designed to dynamically tune the effective receptive field, matching the dynamic structural transformations of organs. By employing a differentiable soft-attention mechanism to aggregate candidate kernels, the network adaptively optimizes its scale sensitivity. This integration allows MSA

Indexed as

Image Processing, Computer-AssistedImaging, Three-DimensionalAlgorithmsConvolutional Neural NetworksHumansMagnetic Resonance ImagingconvolutionCSWin transformerinterrelationship theoryMedical image segmentationmulti-scale information

Identifiers

PMID41940578
PMCPMC13053973

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.