CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentation Article Swipe
Yuanbin Chen
,
Tao Wang
,
Hui Tang
,
Longxuan Zhao
,
Xinlin Zhang
,
Tao Tan
,
Qinquan Gao
,
Anna Min Du
,
Tong Tong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acede8
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/acede8
Medical image segmentation is a crucial and intricate process in medical image processing and analysis. With the advancements in artificial intelligence, deep learning techniques have been widely used in recent years for medical image segmentation. One such technique is the U-Net framework based on the U-shaped convolutional neural networks (CNN) and its variants. However, these methods have limitations in simultaneously capturing both the global and the remote semantic information due to the restricted receptive domain caused by the convolution operation’s intrinsic features. Transformers are attention-based models with excellent global modeling capabilities, but their ability to acquire local information is limited. To address this, we propose a network that combines the strengths of both CNN and Transformer, called CoTrFuse. The proposed CoTrFuse network uses EfficientNet and Swin Transformer as dual encoders. The Swin Transformer and CNN Fusion module are combined to fuse the features of both branches before the skip connection structure. We evaluated the proposed network on two datasets: the ISIC-2017 challenge dataset and the COVID-QU-Ex dataset. Our experimental results demonstrate that the proposed CoTrFuse outperforms several state-of-the-art segmentation methods, indicating its superiority in medical image segmentation. The codes are available at https://github.com/BinYCn/CoTrFuse .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/acede8
- https://iopscience.iop.org/article/10.1088/1361-6560/acede8/pdf
- OA Status
- hybrid
- Cited By
- 37
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386046221
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- OpenAlex ID
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https://openalex.org/W4386046221Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/acede8Digital Object Identifier
- Title
-
CoTrFuse: a novel framework by fusing CNN and transformer for medical image segmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-22Full publication date if available
- Authors
-
Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Xinlin Zhang, Tao Tan, Qinquan Gao, Anna Min Du, Tong TongList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/acede8Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/acede8/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1361-6560/acede8/pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Segmentation, Convolutional neural network, Encoder, Transformer, Deep learning, Image segmentation, Pattern recognition (psychology), Fuse (electrical), Computer vision, Physics, Operating system, Quantum mechanics, Voltage, Electrical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
37Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 13, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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62Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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