Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation
The increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation frameworks. However, existing models often suffer from task-specific limitations, cata...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11000122/ |
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| author | Tareq Mahmod AlZubi Hamza Mukhtar |
| author_facet | Tareq Mahmod AlZubi Hamza Mukhtar |
| author_sort | Tareq Mahmod AlZubi |
| collection | DOAJ |
| description | The increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation frameworks. However, existing models often suffer from task-specific limitations, catastrophic forgetting, and poor scalability due to their dependency on narrowly annotated datasets. This creates a significant gap in developing unified, multi-organ segmentation systems that leverage distributed and partially labeled datasets across diverse clinical institutions. To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. Our model adapts the Segment Anything Model (SAM) for volumetric medical imaging by introducing architectural enhancements, including 3D spatial feature adapters and an Auto Prompt Generator (APG), to optimize spatial representation and reduce reliance on manually crafted prompts. Fed3D-KDNet employs a dual knowledge distillation strategy to mitigate catastrophic forgetting and improve cross-client knowledge transfer, ensuring robust generalization across heterogeneous datasets. The proposed methodology was evaluated on multi-organ CT datasets, including the BTCV benchmark, under centralized and federated settings. Experimental results demonstrate that Fed3D-KDNet achieves state-of-the-art performance with an average Dice score of 80.53% and an average Hausdorff Distance (HD) of 11.43 voxels in federated experiments involving seven clients, showing 5.04% improvement in Dice accuracy and a 4.35 voxel reduction in HD. Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. These results validate the efficacy, scalability, and computational efficiency of Fed3D-KDNet, positioning it as a robust solution for multi-organ medical image segmentation in federated environments. |
| format | Article |
| id | doaj-art-4e967e10b8514c02bbbfddae2cf35ebf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e967e10b8514c02bbbfddae2cf35ebf2025-08-20T03:48:07ZengIEEEIEEE Access2169-35362025-01-0113836198364210.1109/ACCESS.2025.356907311000122Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image SegmentationTareq Mahmod AlZubi0https://orcid.org/0000-0001-9352-2921Hamza Mukhtar1Department of Computer Science, Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, As-Salt, JordanDepartment of Computer Science, University of Engineering and Technology Lahore (UET Lahore), Lahore, PakistanThe increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation frameworks. However, existing models often suffer from task-specific limitations, catastrophic forgetting, and poor scalability due to their dependency on narrowly annotated datasets. This creates a significant gap in developing unified, multi-organ segmentation systems that leverage distributed and partially labeled datasets across diverse clinical institutions. To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. Our model adapts the Segment Anything Model (SAM) for volumetric medical imaging by introducing architectural enhancements, including 3D spatial feature adapters and an Auto Prompt Generator (APG), to optimize spatial representation and reduce reliance on manually crafted prompts. Fed3D-KDNet employs a dual knowledge distillation strategy to mitigate catastrophic forgetting and improve cross-client knowledge transfer, ensuring robust generalization across heterogeneous datasets. The proposed methodology was evaluated on multi-organ CT datasets, including the BTCV benchmark, under centralized and federated settings. Experimental results demonstrate that Fed3D-KDNet achieves state-of-the-art performance with an average Dice score of 80.53% and an average Hausdorff Distance (HD) of 11.43 voxels in federated experiments involving seven clients, showing 5.04% improvement in Dice accuracy and a 4.35 voxel reduction in HD. Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. These results validate the efficacy, scalability, and computational efficiency of Fed3D-KDNet, positioning it as a robust solution for multi-organ medical image segmentation in federated environments.https://ieeexplore.ieee.org/document/11000122/Federated learningmedical image segmentationtransformerknowledge distillation |
| spellingShingle | Tareq Mahmod AlZubi Hamza Mukhtar Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation IEEE Access Federated learning medical image segmentation transformer knowledge distillation |
| title | Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation |
| title_full | Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation |
| title_fullStr | Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation |
| title_full_unstemmed | Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation |
| title_short | Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation |
| title_sort | federated knowledge distillation with 3d transformer adaptation for weakly labeled multi organ medical image segmentation |
| topic | Federated learning medical image segmentation transformer knowledge distillation |
| url | https://ieeexplore.ieee.org/document/11000122/ |
| work_keys_str_mv | AT tareqmahmodalzubi federatedknowledgedistillationwith3dtransformeradaptationforweaklylabeledmultiorganmedicalimagesegmentation AT hamzamukhtar federatedknowledgedistillationwith3dtransformeradaptationforweaklylabeledmultiorganmedicalimagesegmentation |