Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation

• URL: http://arxiv.org/abs/2305.13756v2
• Date: Fri, 16 Jun 2023 23:59:05 GMT
• Title: Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
• Abstract summary: We propose a client-server image segmentation system which allows for the analysis of multi-centric medical images. In this approach, the client protects the to-be-segmented patient image by mixing it to a reference image. Results show that the segmentation accuracy of our method is comparable to a system trained on raw images, and outperforms other privacy-preserving methods with little computational overhead.
• Score: 20.47148375445856
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows for the analysis of multi-centric medical images while preserving patient privacy. In this approach, the client protects the to-be-segmented patient image by mixing it to a reference image. As shown in our work, it is challenging to separate the image mixture to exact original content, thus making the data unworkable and unrecognizable for an unauthorized person. This proxy image is sent to a server for processing. The server then returns the mixture of segmentation maps, which the client can revert to a correct target segmentation. Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture. Furthermore, the whole system is trained end-to-end. The proposed method is validated on the task of MRI brain segmentation using images from two different datasets. Results show that the segmentation accuracy of our method is comparable to a system trained on raw images, and outperforms other privacy-preserving methods with little computational overhead.

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