Enhanced brain tumour segmentation using a hybrid dual encoder–decoder model in federated learning
Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furtherm...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 34416 - 19 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group UK
02.10.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furthermore, data privacy concerns limit centralized model training on large-scale, multi-institutional datasets. To address these drawbacks, we propose a Hybrid Dual Encoder–Decoder Segmentation Model in Federated Learning, that integrates EfficientNet with Swin Transformer as encoders and BASNet (Boundary-Aware Segmentation Network) decoder with MaskFormer as decoders. The proposed model aims to enhance segmentation accuracy and efficiency in terms of total training time. This model leverages hierarchical feature extraction, self-attention mechanisms, and boundary-aware segmentation for superior tumour delineation. The proposed model achieves a Dice Coefficient of 0.94, an Intersection over Union (IoU) of 0.87 and reduces total training time through faster convergence in fewer rounds. The proposed model exhibits strong boundary delineation performance, with a Hausdorff Distance (HD95) of 1.61, an Average Symmetric Surface Distance (ASSD) of 1.12, and a Boundary F1 Score (BF1) of 0.91, indicating precise segmentation contours. Evaluations on the Kaggle Mateuszbuda LGG-MRI segmentation dataset partitioned across multiple federated clients demonstrate consistent, high segmentation performance. These findings highlight that integrating transformers, lightweight CNNs, and advanced decoders within a federated setup supports enhanced segmentation accuracy while preserving medical data privacy. |
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| AbstractList | Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furthermore, data privacy concerns limit centralized model training on large-scale, multi-institutional datasets. To address these drawbacks, we propose a Hybrid Dual Encoder-Decoder Segmentation Model in Federated Learning, that integrates EfficientNet with Swin Transformer as encoders and BASNet (Boundary-Aware Segmentation Network) decoder with MaskFormer as decoders. The proposed model aims to enhance segmentation accuracy and efficiency in terms of total training time. This model leverages hierarchical feature extraction, self-attention mechanisms, and boundary-aware segmentation for superior tumour delineation. The proposed model achieves a Dice Coefficient of 0.94, an Intersection over Union (IoU) of 0.87 and reduces total training time through faster convergence in fewer rounds. The proposed model exhibits strong boundary delineation performance, with a Hausdorff Distance (HD95) of 1.61, an Average Symmetric Surface Distance (ASSD) of 1.12, and a Boundary F1 Score (BF1) of 0.91, indicating precise segmentation contours. Evaluations on the Kaggle Mateuszbuda LGG-MRI segmentation dataset partitioned across multiple federated clients demonstrate consistent, high segmentation performance. These findings highlight that integrating transformers, lightweight CNNs, and advanced decoders within a federated setup supports enhanced segmentation accuracy while preserving medical data privacy.Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furthermore, data privacy concerns limit centralized model training on large-scale, multi-institutional datasets. To address these drawbacks, we propose a Hybrid Dual Encoder-Decoder Segmentation Model in Federated Learning, that integrates EfficientNet with Swin Transformer as encoders and BASNet (Boundary-Aware Segmentation Network) decoder with MaskFormer as decoders. The proposed model aims to enhance segmentation accuracy and efficiency in terms of total training time. This model leverages hierarchical feature extraction, self-attention mechanisms, and boundary-aware segmentation for superior tumour delineation. The proposed model achieves a Dice Coefficient of 0.94, an Intersection over Union (IoU) of 0.87 and reduces total training time through faster convergence in fewer rounds. The proposed model exhibits strong boundary delineation performance, with a Hausdorff Distance (HD95) of 1.61, an Average Symmetric Surface Distance (ASSD) of 1.12, and a Boundary F1 Score (BF1) of 0.91, indicating precise segmentation contours. Evaluations on the Kaggle Mateuszbuda LGG-MRI segmentation dataset partitioned across multiple federated clients demonstrate consistent, high segmentation performance. These findings highlight that integrating transformers, lightweight CNNs, and advanced decoders within a federated setup supports enhanced segmentation accuracy while preserving medical data privacy. Abstract Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furthermore, data privacy concerns limit centralized model training on large-scale, multi-institutional datasets. To address these drawbacks, we propose a Hybrid Dual Encoder–Decoder Segmentation Model in Federated Learning, that integrates EfficientNet with Swin Transformer as encoders and BASNet (Boundary-Aware Segmentation Network) decoder with MaskFormer as decoders. The proposed model aims to enhance segmentation accuracy and efficiency in terms of total training time. This model leverages hierarchical feature extraction, self-attention mechanisms, and boundary-aware segmentation for superior tumour delineation. The proposed model achieves a Dice Coefficient of 0.94, an Intersection over Union (IoU) of 0.87 and reduces total training time through faster convergence in fewer rounds. The proposed model exhibits strong boundary delineation performance, with a Hausdorff Distance (HD95) of 1.61, an Average Symmetric Surface Distance (ASSD) of 1.12, and a Boundary F1 Score (BF1) of 0.91, indicating precise segmentation contours. Evaluations on the Kaggle Mateuszbuda LGG-MRI segmentation dataset partitioned across multiple federated clients demonstrate consistent, high segmentation performance. These findings highlight that integrating transformers, lightweight CNNs, and advanced decoders within a federated setup supports enhanced segmentation accuracy while preserving medical data privacy. Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning. However, conventional segmentation models often struggle with boundary delineation and generalization across heterogeneous datasets. Furthermore, data privacy concerns limit centralized model training on large-scale, multi-institutional datasets. To address these drawbacks, we propose a Hybrid Dual Encoder-Decoder Segmentation Model in Federated Learning, that integrates EfficientNet with Swin Transformer as encoders and BASNet (Boundary-Aware Segmentation Network) decoder with MaskFormer as decoders. The proposed model aims to enhance segmentation accuracy and efficiency in terms of total training time. This model leverages hierarchical feature extraction, self-attention mechanisms, and boundary-aware segmentation for superior tumour delineation. The proposed model achieves a Dice Coefficient of 0.94, an Intersection over Union (IoU) of 0.87 and reduces total training time through faster convergence in fewer rounds. The proposed model exhibits strong boundary delineation performance, with a Hausdorff Distance (HD95) of 1.61, an Average Symmetric Surface Distance (ASSD) of 1.12, and a Boundary F1 Score (BF1) of 0.91, indicating precise segmentation contours. Evaluations on the Kaggle Mateuszbuda LGG-MRI segmentation dataset partitioned across multiple federated clients demonstrate consistent, high segmentation performance. These findings highlight that integrating transformers, lightweight CNNs, and advanced decoders within a federated setup supports enhanced segmentation accuracy while preserving medical data privacy. |
| ArticleNumber | 34416 |
| Author | Narmadha, K. Varalakshmi, P. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41038951$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/s40537-019-0276-2 10.1007/978-3-030-00889-5_1 10.3390/s23073420 10.1007/978-3-319-24574-4_28 10.3390/bdcc8090099 10.1016/j.imavis.2025.105463 10.1007/s10278-024-00981-7 10.1109/3DV.2016.79 10.3390/diagnostics15050513 10.1007/978-3-319-55524-9_14 10.7717/peerj-cs.1754 10.1007/978-3-030-61609-0_60 10.1016/j.compbiomed.2021.104472 10.1016/j.bspc.2023.104791 10.1109/BIBM62325.2024.10822476 10.3390/mti2030047 10.3390/electronics12071687 10.1016/j.compmedimag.2023.102313 10.1109/ACCESS.2021.3111131 10.1109/TPAMI.2024.3435571 10.3390/bioengineering9080368 10.3390/diagnostics13193140 10.1016/j.patcog.2024.110424 10.1016/j.knosys.2025.113785 10.1109/PAIS62114.2024.10541292 10.1145/3298981 10.1016/j.ijcce.2022.11.001 10.1109/ICCV48922.2021.00986 10.1016/j.eswa.2023.122093 10.1109/WACV51458.2022.00181 10.1007/978-3-031-15937-4_65 |
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| Snippet | Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment planning.... Abstract Brain tumour segmentation is an important task in medical imaging, that requires accurate tumour localization for improved diagnostics and treatment... |
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| SubjectTerms | Accuracy Algorithms BASNet Brain cancer Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain research Brain tumors Brain tumour segmentation Communication Data integrity Datasets Deep learning Efficiency EfficientNet Federated Learning Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Localization Magnetic Resonance Imaging - methods MaskFormer Medical imaging multidisciplinary Neural Networks, Computer Neuroimaging Privacy Science Science (multidisciplinary) Segmentation Swin transformer Training |
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| Title | Enhanced brain tumour segmentation using a hybrid dual encoder–decoder model in federated learning |
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