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Few-shot image segmentation based on multi-scale feature fusion and contrastive learning
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Xiaofei HU1, Jiayun WU2, Guichun ZOU2, Lingzhi WU3
Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition) | 2025, 45(5) : 66 - 73
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Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition) | 2025, 45(5): 66-73
Computer and Automation
Few-shot image segmentation based on multi-scale feature fusion and contrastive learning
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Xiaofei HU1, Jiayun WU2, Guichun ZOU2, Lingzhi WU3
Affiliations
  • 1.School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • 2.School of Chemistry and Life Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • 3.College of Science, Nanjing University of Posts and Telecommunications,Nanjing 210023, China
doi: 10.14132/j.cnki.1673-5439.2025.05.008
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In response to the scarcity of annotated medical image data and the imitations of existing models in segmenting multi-scale target images, this paper proposes a few-shot medical image segmentation method based on multi-scale feature fusion and contrastive learning. First, a sequential concatenation-based multi-scale skip connection method is introduced to replace traditional skip connections, enabling effective fusion of multi-scale feature maps from the encoder and their transmission to the corresponding decoder. Second, considering the dual-branch structure of the model, a contrastive learning module based on multi-scale features is proposed, and a loss function is designed to enhance the model's discriminative ability at the pixel level. Experiments show that our method achieves cross-domain data segmentation for medical images, mitigates performance degradation due to dataset scarcity, and improves the segmentation accuracy and generalization for different-scale targets , outperforming current mainstream few-shot medical image segmentation methods.

deep learning  /  medical image segmentation  /  multi-scale feature fusion  /  contrastive learning  /  few-shot learning
Xiaofei HU, Jiayun WU, Guichun ZOU, Lingzhi WU. Few-shot image segmentation based on multi-scale feature fusion and contrastive learning[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition), 2025 , 45 (5) : 66 -73 . DOI: 10.14132/j.cnki.1673-5439.2025.05.008
Year 2025 volume 45 Issue 5
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doi: 10.14132/j.cnki.1673-5439.2025.05.008
  • Receive Date:2024-09-30
  • Online Date:2026-04-16
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  • Received:2024-09-30
  • Revised:2024-12-21
Affiliations
    1.School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    2.School of Chemistry and Life Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
    3.College of Science, Nanjing University of Posts and Telecommunications,Nanjing 210023, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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