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Privacy-aware deep vein thrombosis segmentation using a multi-model federated learning framework with the federated

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Summary

This study introduces a Federated Learning (FedL) approach for accurate Deep Vein Thrombosis (DVT) segmentation using Computer Tomography (CT) scans. The FedAvg algorithm improved model performance while preserving data privacy across diverse datasets and client models.

Keywords:
CTDeep vein thrombosis segmentationFederated averagingFederated learningModel aggregationNon-IID data

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Deep Vein Thrombosis (DVT) diagnosis relies on precise segmentation of Computer Tomography (CT) scans.
  • Existing segmentation methods may face challenges with data privacy and distributed datasets.
  • Federated Learning (FedL) offers a privacy-preserving approach for training models on decentralized data.

Purpose of the Study:

  • To develop and evaluate an efficient Federated Learning (FedL) architecture for DVT segmentation using CT images.
  • To enhance segmentation accuracy and efficiency while maintaining data privacy and security.
  • To assess the scalability and stability of the FedL framework across varying dataset sizes and model complexities.

Main Methods:

  • Proposed an efficient FedL architecture utilizing the Federated Averaging (FedAvg) algorithm.
  • Trained seven distinct local models on non-independent and identically distributed (Non-IID) CT images across three phases with increasing client numbers and model diversity (CNN, Sequential, Semantic, U-Net, VGG Net-19, Modified U-Net, Modified-Net).
  • Aggregated local model weights to progressively improve a global model, evaluated on datasets of 1000, 2000, and 3000 samples.

Main Results:

  • Significant performance gains observed with increasing dataset size, including higher Accuracy and F1-score, and decreased Tversky Loss.
  • Consistent improvement across all phases, with validation loss reducing from 0.910 to 0.061.
  • Framework demonstrated scalability with increased communication costs (14 MB to 3279 MB) and training time (7.67 s to 18,702 s), while preserving differential privacy and improving client heterogeneity.

Conclusions:

  • The proposed FedL framework effectively enhances DVT segmentation accuracy and efficiency on CT images.
  • The architecture demonstrates robust scalability, stability, and differential privacy preservation in heterogeneous environments.
  • Federated Learning is a viable approach for privacy-preserving medical image analysis in distributed settings.