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Abstract
This article proposes a mask refinement method for chromosome instance segmentation. The proposed method exploits the knowledge representation capability of Neural Knowledge DNA (NK-DNA) to capture the semantics of the chromosome’s shape, texture, and key points, and then it uses the captured knowledge to improve the accuracy and smoothness of the masks. We validate the method’s effectiveness on our latest high-resolution chromosome image dataset. The experimental results show that our proposed method’s mask average precision (MaskAP) is 3.66% higher than Mask R-CNN and outperforms advanced Cascade Mask R-CNN by 1.35%.
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Authors (3)
-
Ding Chen
- Chengdu University, China
-
Haoxi Zhang
- Chengdu University, China
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2022.2162741
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 55,
pages 708 - 718,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Chen D., Zhang H., Szczerbicki E.: KEMR-Net: A Knowledge-Enhanced Mask Refinement Network for Chromosome Instance Segmentation// CYBERNETICS AND SYSTEMS -Vol. 55,iss. 3 (2024), s.708-718
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2022.2162741
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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