Abstract

Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks. However, the feasibility of this attack type has not been investigated for complex 3D medical images. Thus, the aim of this study was to examine the vulnerability of deep learning techniques used in medical imaging to model inversion attacks and investigate multiple quantitative metrics to evaluate the quality of the reconstructed images. For the development and evaluation of model inversion attacks, the public LPBA40 database consisting of 40 brain MRI scans with corresponding segmentations of the gyri and deep grey matter brain structures were used to train two popular deep convolutional neural networks, namely a U-Net and SegNet, and corresponding inversion decoders. Matthews correlation coefficient, the structural similarity index measure (SSIM), and the magnitude of the deformation field resulting from non-linear registration of the original and reconstructed images were used to evaluate the reconstruction accuracy. A comparison of the similarity metrics revealed that the SSIM is best suited to evaluate the reconstruction accuray, followed closely by the magnitude of the deformation field. The quantitative evaluation of the reconstructed images revealed SSIM scores of and for the U-Net and the SegNet, respectively. The qualitative evaluation showed that training images can be reconstructed with some degradation due to blurring but can be correctly matched to the original images in the majority of the cases. In conclusion, the results of this study indicate that it is possible to reconstruct patient data used for training of convolutional neural networks and that the SSIM is a good metric to assess the reconstruction accuracy.

Highlights

  • Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks

  • Deep learning techniques have revolutionized medical image analysis as they can learn from large amounts of data and often offer advantages in accuracy compared to conventional machine learning methods

  • Those results indicate that both structural similarity index measure (SSIM) and Matthews correlation coefficient (MCC) in general reflect the visual quality of the reconstructed image in comparison to the original image

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Summary

Introduction

Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks. The aim of this study was to examine the vulnerability of deep learning techniques used in medical imaging to model inversion attacks and investigate multiple quantitative metrics to evaluate the quality of the reconstructed images. The structural similarity index measure (SSIM), and the magnitude of the deformation field resulting from non-linear registration of the original and reconstructed images were used to evaluate the reconstruction accuracy. The results of this study indicate that it is possible to reconstruct patient data used for training of convolutional neural networks and that the SSIM is a good metric to assess the reconstruction accuracy. One of the most important aspects when using medical images to train deep neural networks is patient data confidentiality. Patient data does include the imaging data itself (X-ray, CT, MRI, PET, etc.), but may include other relevant attributes used for training of deep neural networks, such as age, sex, medical condition (high blood pressure, diabetes, etc.), and life style factors (smoking, alcohol, etc.)

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