FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation
To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. In the encoder part, a dual‐stream encoder is used to capture local details and lo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400201 |
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| _version_ | 1850243543575035904 |
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| author | Yuhan Ding Jinhui Liu Yunbo He Jinliang Huang Haisu Liang Zhenglin Yi Yongjie Wang |
| author_facet | Yuhan Ding Jinhui Liu Yunbo He Jinliang Huang Haisu Liang Zhenglin Yi Yongjie Wang |
| author_sort | Yuhan Ding |
| collection | DOAJ |
| description | To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. In the encoder part, a dual‐stream encoder is used to capture local details and long‐range dependencies. Moreover, the attentional feature fusion module is used to perform interactive feature fusion of dual‐branch features, maximizing the retention of local details and global semantic information in medical images. At the same time, the multi‐scale feature aggregation module is used to aggregate local information and capture multi‐scale context to mine more semantic details. The multi‐level feature bridging module is used in skip connections to bridge multi‐level features and mask information to assist multi‐scale feature interaction. Experimental results on seven public medical image datasets fully demonstrate the effectiveness and advancement of our method. In future work, we plan to extend FI‐Net to support 3D medical image segmentation tasks and combine self‐supervised learning and knowledge distillation to alleviate the overfitting problem of limited data training. |
| format | Article |
| id | doaj-art-b9ae2866e15243e0bcc8cdc9eb018a27 |
| institution | OA Journals |
| issn | 2640-4567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-b9ae2866e15243e0bcc8cdc9eb018a272025-08-20T01:59:57ZengWileyAdvanced Intelligent Systems2640-45672024-12-01612n/an/a10.1002/aisy.202400201FI‐Net: Rethinking Feature Interactions for Medical Image SegmentationYuhan Ding0Jinhui Liu1Yunbo He2Jinliang Huang3Haisu Liang4Zhenglin Yi5Yongjie Wang6School of Computer Science and Engineering Central South University Changsha 410000 ChinaDepartments of Urology Xiangya Hospital Central South University Changsha 410008 ChinaDepartments of Urology Xiangya Hospital Central South University Changsha 410008 ChinaDepartments of Urology Xiangya Hospital Central South University Changsha 410008 ChinaDepartments of Urology Xiangya Hospital Central South University Changsha 410008 ChinaDepartments of Urology Xiangya Hospital Central South University Changsha 410008 ChinaDepartment of Burns and Plastic Surgery Xiangya Hospital Central South University Changsha 410008 ChinaTo solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI‐Net based on CNN‐Transformer for medical image segmentation. In the encoder part, a dual‐stream encoder is used to capture local details and long‐range dependencies. Moreover, the attentional feature fusion module is used to perform interactive feature fusion of dual‐branch features, maximizing the retention of local details and global semantic information in medical images. At the same time, the multi‐scale feature aggregation module is used to aggregate local information and capture multi‐scale context to mine more semantic details. The multi‐level feature bridging module is used in skip connections to bridge multi‐level features and mask information to assist multi‐scale feature interaction. Experimental results on seven public medical image datasets fully demonstrate the effectiveness and advancement of our method. In future work, we plan to extend FI‐Net to support 3D medical image segmentation tasks and combine self‐supervised learning and knowledge distillation to alleviate the overfitting problem of limited data training.https://doi.org/10.1002/aisy.202400201convolutional neural networkfeature interactionmedical image segmentationmulti‐scale featuretransformer |
| spellingShingle | Yuhan Ding Jinhui Liu Yunbo He Jinliang Huang Haisu Liang Zhenglin Yi Yongjie Wang FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation Advanced Intelligent Systems convolutional neural network feature interaction medical image segmentation multi‐scale feature transformer |
| title | FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation |
| title_full | FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation |
| title_fullStr | FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation |
| title_full_unstemmed | FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation |
| title_short | FI‐Net: Rethinking Feature Interactions for Medical Image Segmentation |
| title_sort | fi net rethinking feature interactions for medical image segmentation |
| topic | convolutional neural network feature interaction medical image segmentation multi‐scale feature transformer |
| url | https://doi.org/10.1002/aisy.202400201 |
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