Federated Learning Model Using CBDPL for Medical Image Segmentation
Federated learning (FL) allows hospitals and medical centers to train models without sharing patient data. Instead of sending data to a central server, each institution trains the model locally and only shares updates, keeping patient information private. Our study proposes a novel methodology for m...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11025474/ |
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| Summary: | Federated learning (FL) allows hospitals and medical centers to train models without sharing patient data. Instead of sending data to a central server, each institution trains the model locally and only shares updates, keeping patient information private. Our study proposes a novel methodology for medical image segmentation that leverages semi-supervised learning and confidence-based dynamic pseudo-labeling (CBDPL) to enhance model performance with limited labeled data. Each participating client site independently performs feature extraction and basic segmentation tasks. For labeled data, segmentation is guided by a ground-truth comparison and loss minimization. For unlabeled data, semi-supervised learning is applied, with a dynamic pseudo-labeling module that assigns pseudo-labels based on confidence scores, integrating only high-confidence predictions to refine the model iteratively. Client updates are then combined on a central server. It helps to improve the overall model while keeping each client’s data private. This approach demonstrates the potential for robust, privacy-preserving segmentation in multi-dataset medical image analysis settings, addressing labeled data scarcity and privacy concerns. |
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| ISSN: | 2169-3536 |