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ChestFed: Model-Contrastive Federated Learning for Cardiopulmonary Disease Classification in Chest X-rays using Multiple Datasets
Tianhao Li · Ajay Jaiswal · Justin Rousseau · Ying Ding

Artificial intelligence in medical imaging has emerged to be a topic with high demand in medical practice in the recent years. However, limited data availability due to strict patient privacy policy becomes a main barrier in this area. Federated learning enables multiple parties to collaboratively train a machine learning/deep learning model without sharing their local data. Model-contrastive federated learning, as a novel federated learning framework, is designed to handle the heterogeneity of local data distribution by using contrastive learning across parties. In this work, we applied the model-contrastive federated learning in multiple chest x-ray datasets to derive a global model for disease diagnosis. Our experiment shows that using federated learning only on two datasets, our model outperforms the model trained in one single dataset by 4%, which indicates the potential to apply federated learning on several chest x-ray datasets to achieve higher accuracy without the need to share local data.

Author Information

Tianhao Li (The University of Texas at Austin)
Ajay Jaiswal (UT Austin)
Justin Rousseau (Dell Medical School at University of Texas at Austin)
Ying Ding (University of Texas at Austin)

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