Collaborative Deep Learning for Medical Image Analysis with Differential Privacy

Abstract

Deep learning algorithms, especially convolution neural networks, have attracted huge attention in the field of medical image analysis. A hospital could train a neural network to detect disease based on medical images possessing. However, the number of medical images would affect the results of training. If medical images of all hospitals are collected together, there's a risk of privacy leakage. In this paper, we apply collaborative deep learning to medical image analysis, which could help to improve the training effect. Besides, we also exploit differential privacy, the analytic Gaussian Mechanism, to prevent the leakage of information about medical images. We experiment on the Chest X-ray Images (Pneumonia) dataset. Results show that the analytic Gaussian Mechanism can protect the privacy of medical images effectively, while the influence on the results of training is small. The accuracy can be improved about 19% via collaborative deep learning and can still remain about 18% even when the analytic Gaussian Mechanism was used.