Evidence map›Paper›PMID 40567797›Full record

ArticlePeerJ. Computer science2025

Enhancing colorectal polyp classification using gaze-based attention networks.

Zhenghao Guo, Yanyan Hu, Peixuan Ge, In Neng Chan, Tao Yan, Pak Kin Wong, Shaoyong Xu, Zheng Li, Shan Gao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. 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

9 authors.

Zhenghao Guo *School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, China.
Yanyan Hu *Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China.
Peixuan GeDepartment of Electromechanical Engineering, University of Macau, Taipa, Macao, China.
In Neng ChanDepartment of Electromechanical Engineering, University of Macau, Taipa, Macao, China.
Tao YanSchool of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, China.
Pak Kin WongDepartment of Electromechanical Engineering, University of Macau, Taipa, Macao, China.ORCID 0000-0002-7623-6904
Shaoyong XuXiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China.
Zheng LiXiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China.
Shan GaoXiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal polyps are potential precursor lesions of colorectal cancer. Accurate classification of colorectal polyps during endoscopy is crucial for early diagnosis and effective treatment. Automatic and accurate classification of colorectal polyps based on convolutional neural networks (CNNs) during endoscopy is vital for assisting endoscopists in diagnosis and treatment. However, this task remains challenging due to difficulties in the data acquisition and annotation processes, the poor interpretability of the data output, and the lack of widespread acceptance of the CNN models by clinicians. This study proposes an innovative approach that utilizes gaze attention information from endoscopists as an auxiliary supervisory signal to train a CNN-based model for the classification of colorectal polyps. Gaze information from the reading of endoscopic images was first recorded through an eye-tracker. Then, the gaze information was processed and applied to supervise the CNN model's attention via an

Indexed as

Class activation mapColorectal polypsEye-trackingGaze attention

Identifiers

PMID40567797
PMCPMC12190586

What Socratic holds

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LicenceCC BY
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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 Socratic graph.