A semi-supervised fracture-attention model for segmenting tubular objects with improved topological connectivity

Authors

Ge Yang (ORCID: https://orcid.org/0000-0001-6176-3130)
Liqun Zhong
Yanfeng Zhou (ORCID: https://orcid.org/0000-0001-5103-2879)
Zichen Wang (ORCID: https://orcid.org/0009-0002-7402-4323)

Institutions

Chinese Academy of Sciences (CN)
Shandong Institute of Automation (CN)
Beijing Academy of Artificial Intelligence (CN)
Institute of Automation (CN)
University of Chinese Academy of Sciences (CN)

Publication Details

Journal: Bioinformatics
Published: 2024-12-26
DOI: https://doi.org/10.1093/bioinformatics/btaf013
Citations: 1
Primary Topic: Anomaly Detection Techniques and Applications
Type: article
Field-Weighted Citation Impact: 0.35

Funders

National Natural Science Foundation of China
Chinese Academy of Sciences
Fundamental Research Funds for the Central Universities

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article

A semi-supervised fracture-attention model for segmenting tubular objects with improved topological connectivity

Ge Yang, Liqun Zhong, Yanfeng Zhou, Zichen Wang

Dec 26, 2024

1 citations

Bioinformatics

Anomaly Detection Techniques and Applications

0.35

article

A semi-supervised fracture-attention model for segmenting tubular objects with improved topological connectivity

Ge Yang, Liqun Zhong, Yanfeng Zhou, Zichen Wang

December 26, 2024article en

1 citations

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(1)

Chinese Academy of Sciences (CN), Shandong Institute of Automation (CN), Beijing Academy of Artificial Intelligence (CN), Institute of Automation (CN), University of Chinese Academy of Sciences (CN)

National Natural Science Foundation of China, Chinese Academy of Sciences, Fundamental Research Funds for the Central Universities

Openalex Percentile: Top 30%

Anomaly Detection Techniques and Applications

0.35

DOI Open Access PDF OpenAlex

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