CA-SegNet: A channel-attention encoder–decoder network for histopathological image segmentation
Histopathological image segmentation based on encoder–decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may...
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| Veröffentlicht in: | Biomedical signal processing and control Jg. 96; S. 106590 |
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| Sprache: | Englisch |
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Elsevier Ltd
01.10.2024
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| ISSN: | 1746-8094 |
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| Abstract | Histopathological image segmentation based on encoder–decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may burden the learning and even adversely affect the decoder. While various variants have been developed to cope with this issue, the performance remains unsatisfactory. In this work, we propose a novel encoder–decoder architecture named CA-SegNet to address the above issue more effectively and achieve advanced histopathological image segmentation. Our novelty is twofold: firstly, a bottleneck-structured decoder is developed to improve the integration of multi-channel feature representations, and secondly, a sequence of channel-attention feature fusion modules (CAFFMs) are developed to adaptively guide the reuse of fine-grained shallow features in skip connections while learning the channel-wise dependencies. Experimental results on different publicly available histopathological image datasets demonstrate that our CA-SegNet outperforms existing state-of-the-art methods on both large and small-scale datasets.
•A novel deep learning-based CA-SegNet model for histopathological image segmentation.•A channel-attention feature fusion module to greatly improve shallow feature reuse.•A bottleneck-structured decoder for better feature integration.•Outstanding segmentation performance on both large and small-scale datasets. |
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| AbstractList | Histopathological image segmentation based on encoder–decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may burden the learning and even adversely affect the decoder. While various variants have been developed to cope with this issue, the performance remains unsatisfactory. In this work, we propose a novel encoder–decoder architecture named CA-SegNet to address the above issue more effectively and achieve advanced histopathological image segmentation. Our novelty is twofold: firstly, a bottleneck-structured decoder is developed to improve the integration of multi-channel feature representations, and secondly, a sequence of channel-attention feature fusion modules (CAFFMs) are developed to adaptively guide the reuse of fine-grained shallow features in skip connections while learning the channel-wise dependencies. Experimental results on different publicly available histopathological image datasets demonstrate that our CA-SegNet outperforms existing state-of-the-art methods on both large and small-scale datasets.
•A novel deep learning-based CA-SegNet model for histopathological image segmentation.•A channel-attention feature fusion module to greatly improve shallow feature reuse.•A bottleneck-structured decoder for better feature integration.•Outstanding segmentation performance on both large and small-scale datasets. A novel deep learning-based CA-SegNet model for histopathological image segmentation.• A channel-attention feature fusion module (CAFFM) that significantly improves shallow feature reuse.• A bottleneck-structured decoder developed for better feature integration.• Outstanding segmentation performance on both large and small-scale datasets. |
| ArticleNumber | 106590 |
| Author | Liu, Zhengjun He, Feng Zhu, Yuemin Ren, Lijuan Wang, Weibo Zhao, Yixuan |
| Author_xml | – sequence: 1 givenname: Feng orcidid: 0000-0003-1013-0661 surname: He fullname: He, Feng email: fenghe@hit.edu.cn organization: Institute of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin, 150001, China – sequence: 2 givenname: Weibo surname: Wang fullname: Wang, Weibo email: wwbhit@hit.edu.cn organization: Institute of Ultra-precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin, 150001, China – sequence: 3 givenname: Lijuan surname: Ren fullname: Ren, Lijuan email: renlijuan@cuit.edu.cn organization: School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China – sequence: 4 givenname: Yixuan surname: Zhao fullname: Zhao, Yixuan email: zhaoyixuan@hit.edu.cn organization: Research Center of Advanced Microscopy and Instrumentation, Harbin Institute of Technology, Harbin, 150001, China – sequence: 5 givenname: Zhengjun surname: Liu fullname: Liu, Zhengjun email: zjliu@hit.edu.cn organization: School of Physics, Harbin Institute of Technology, Harbin, 150001, China – sequence: 6 givenname: Yuemin orcidid: 0000-0001-6814-1449 surname: Zhu fullname: Zhu, Yuemin email: yue-min.zhu@creatis.insa-lyon.fr organization: CREATIS, INSA Lyon, CNRS UMR 5220, INSERM U1294, Universit de Lyon, Villeurbanne, 69621, France |
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| Keywords | Deep learning Encoder–decoder Channel attention Histopathological images Medical image segmentation Deep learning Channel attention Encoder-decoder Medical image segmentation Histopathological images |
| Language | English |
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| Title | CA-SegNet: A channel-attention encoder–decoder network for histopathological image segmentation |
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