Evidence map›Paper›PMID 42897418›Full record

ArticleJournal of imaging informatics in medicine2026

SVF-net: A Dual-encoder Network with Adaptive Gated Fusion for Colorectal Polyp Segmentation.

Zepeng Zhang, Long Yu, Shengwei Tian

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 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

3 authors.

Zepeng ZhangSchool of Software, Xinjiang University, Urumqi, 830046, Xinjiang Uygur Autonomous Region, China.ORCID http://orcid.org/0009-0005-1832-1288
Long YuXinjiang Key Laboratory of Intelligent Computing and Smart Applications, Xinjiang University, Urumqi, 830046, Xinjiang Uygur Autonomous Region, China. yul@xju.edu.cn.ORCID http://orcid.org/0000-0002-9038-4129
Shengwei TianNetwork Center, Xinjiang University, Urumqi, 830046, Xinjiang Uygur Autonomous Region, China.ORCID http://orcid.org/0000-0003-3525-5102

Funding

National Natural Science Foundation of China 62162058National Natural Science Foundation of China 62362061Tianshan Talent Training Program 2023TSYCLJ0023
6 · The paper itself

Abstract

Automatic colorectal polyp segmentation in retrospective still colonoscopy images remains challenging for small, low-contrast, partially occluded, or irregular lesions. We propose SVF-Net, a heterogeneous dual-encoder network combining a frozen SAM2 Hiera-L image trunk with a trainable MaxViT-RMLP-Small specialist branch. At four feature scales, the two streams are projected to a common width, spatially aligned, and reconciled by complementary channel-spatial gating before progressive decoding. Across five public benchmarks, three-run mean mDice is 0.922 on CVC-ClinicDB, 0.898 on Kvasir-SEG, 0.805 on CVC-ColonDB, 0.770 on ETIS-LaribPolypDB, and 0.896 on CVC-300. Matched projected-concatenation and fixed-weight (

Indexed as

Adaptive gated fusionColorectal polyp segmentationDual-encoder networkMedical image segmentationSAM2

Identifiers

PMID42897418

What OpenQuestion holds

Textmetadata
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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.