Evidence map›Paper›PMID 38855696›Full record

ArticleBiomedical optics express2024

BFE-Net: bilateral fusion enhanced network for gastrointestinal polyp segmentation.

Kaixuan Zhang, Dingcan Hu, Xiang Li, Xiaotong Wang, Xiaoming Hu, Chunyang Wang, Jinlin Yang, Nini Rao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2024. 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

8 authors.

Kaixuan ZhangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID https://orcid.org/0009-0001-5894-6507
Dingcan HuSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xiang LiSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xiaotong WangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Xiaoming HuSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Chunyang WangSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Jinlin YangDigestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu 610017, China.
Nini RaoSchool of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.ORCID https://orcid.org/0000-0001-7979-2917

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate segmentation of polyp regions in gastrointestinal endoscopic images is pivotal for diagnosis and treatment. Despite advancements, challenges persist, like accurately segmenting small polyps and maintaining accuracy when polyps resemble surrounding tissues. Recent studies show the effectiveness of the pyramid vision transformer (PVT) in capturing global context, yet it may lack detailed information. Conversely, U-Net excels in semantic extraction. Hence, we propose the bilateral fusion enhanced network (BFE-Net) to address these challenges. Our model integrates U-Net and PVT features via a deep feature enhancement fusion module (FEF) and attention decoder module (AD). Experimental results demonstrate significant improvements, validating our model's effectiveness across various datasets and modalities, promising advancements in gastrointestinal polyp diagnosis and treatment.

Identifiers

PMID38855696
PMCPMC11161362

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Registered trials

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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.