Deep anonymization of medical imaging

Lobna Fezai, Thierry Urruty, Pascal Bourdon, Christine Fernandez-Maloigne

Abstract: Deep learning has shown record-shattering performance in multiple medical tasks. However, data quantity and quality are crucial requirements. As a matter of fact, data is one of the most challenging issues while deploying deep learning models for different tasks. One of the main challenges is the institutions’ privacy protocols, in particular in the medical field. Indeed, the metadata is usually excluded from the database provided. Many invisible features in images can help tracing anonymized data. We propose to use deep learning to exclude these traces. This article focuses on Magnetic resonance imaging (MRI) and one of the most important features, the equipment used for acquisition. First, we aim to produce an algorithm able to perform well distinguishing multiple MRI equipment from different brands. To this end, we employ a convolution neural network architecture to work on this medical image classification task. The second part of this paper is dedicated to reconstructing the input MRI using a simple auto-encoder. The latter step is to use the auto-encoder in order to mislead the classifier classifying the MRI equipment.