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Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis (2209.04338v2)
Published 9 Sep 2022 in eess.IV, cs.CR, cs.CV, and cs.LG
Abstract: Machine learning with formal privacy-preserving techniques like Differential Privacy (DP) allows one to derive valuable insights from sensitive medical imaging data while promising to protect patient privacy, but it usually comes at a sharp privacy-utility trade-off. In this work, we propose to use steerable equivariant convolutional networks for medical image analysis with DP. Their improved feature quality and parameter efficiency yield remarkable accuracy gains, narrowing the privacy-utility gap.
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