Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning
The increasing demand for accurate and efficient medical image segmentation has driven the development of advanced Artificial Intelligence (AI) models to assist in diagnosing and planning treatments. In this paper, we propose a novel approach that combines U-Net architecture with attention mechanism...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10960382/ |
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| author | Neha Tirpude Tausif Diwan Meera Dhabu |
| author_facet | Neha Tirpude Tausif Diwan Meera Dhabu |
| author_sort | Neha Tirpude |
| collection | DOAJ |
| description | The increasing demand for accurate and efficient medical image segmentation has driven the development of advanced Artificial Intelligence (AI) models to assist in diagnosing and planning treatments. In this paper, we propose a novel approach that combines U-Net architecture with attention mechanisms and Model-Agnostic Meta-Learning (MAML) to enhance segmentation performance, particularly in scenarios with limited annotated data. By applying this method to the AMOS dataset for multi-organ segmentation in CT and MRI scans, our model significantly improves generalization across different patients and imaging modalities. The use of attention mechanisms refines feature extraction, while class weighting effectively addresses class imbalance. The proposed model achieves a state-of-the-art Dice score of 98.09%, pixel accuracy of 98.39%, precision of 98.58% and recall of 98.27% outperforming existing methods and demonstrating the potential for rapid adaptation in clinical applications. This combination of meta-learning and deep learning offers a robust solution for addressing the challenges of medical image segmentation. |
| format | Article |
| id | doaj-art-98d59bcbd8bb43c2a6e6379a5f155c7e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-98d59bcbd8bb43c2a6e6379a5f155c7e2025-08-20T03:18:27ZengIEEEIEEE Access2169-35362025-01-0113648226483110.1109/ACCESS.2025.355944610960382Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-LearningNeha Tirpude0https://orcid.org/0000-0002-8830-2630Tausif Diwan1Meera Dhabu2Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, IndiaDepartment of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, IndiaDepartment of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, IndiaThe increasing demand for accurate and efficient medical image segmentation has driven the development of advanced Artificial Intelligence (AI) models to assist in diagnosing and planning treatments. In this paper, we propose a novel approach that combines U-Net architecture with attention mechanisms and Model-Agnostic Meta-Learning (MAML) to enhance segmentation performance, particularly in scenarios with limited annotated data. By applying this method to the AMOS dataset for multi-organ segmentation in CT and MRI scans, our model significantly improves generalization across different patients and imaging modalities. The use of attention mechanisms refines feature extraction, while class weighting effectively addresses class imbalance. The proposed model achieves a state-of-the-art Dice score of 98.09%, pixel accuracy of 98.39%, precision of 98.58% and recall of 98.27% outperforming existing methods and demonstrating the potential for rapid adaptation in clinical applications. This combination of meta-learning and deep learning offers a robust solution for addressing the challenges of medical image segmentation.https://ieeexplore.ieee.org/document/10960382/AMOS datasetattention mechanismsmedical image segmentationmeta-learningU-Net architecture |
| spellingShingle | Neha Tirpude Tausif Diwan Meera Dhabu Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning IEEE Access AMOS dataset attention mechanisms medical image segmentation meta-learning U-Net architecture |
| title | Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning |
| title_full | Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning |
| title_fullStr | Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning |
| title_full_unstemmed | Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning |
| title_short | Meta-Transformers: A Hybrid Approach for Medical Image Segmentation With U-Net and Meta-Learning |
| title_sort | meta transformers a hybrid approach for medical image segmentation with u net and meta learning |
| topic | AMOS dataset attention mechanisms medical image segmentation meta-learning U-Net architecture |
| url | https://ieeexplore.ieee.org/document/10960382/ |
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