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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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 34416 - 19
Hauptverfasser: Narmadha, K., Varalakshmi, P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 02.10.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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
Brain tumour segmentation
Swin transformer
EfficientNet
Federated learning
BASNet
MaskFormer
ISSN:2045-2322, 2045-2322
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Zusammenfassung: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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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-17432-0