scholarly journals Deep Convolutional Neural Networks for Predicting Head Pose During Brain MRI Acquisition

2020 ◽  
Vol 20 (11) ◽  
pp. 817
Author(s):  
Yijun Zhao ◽  
Hui Yuan ◽  
Jingjie Zhou ◽  
Samantha Martin ◽  
Heath Pardoe
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Brain Mri ◽  
Head Pose ◽  
Deep Convolutional Neural Networks

scholarly journals An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3874
Author(s):  
Nagesh Subbanna ◽  
Matthias Wilms ◽  
Anup Tuladhar ◽  
Nils D. Forkert
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Brain Mri ◽  
Similarity Index ◽  
Medical Image Segmentation ◽  
Deformation Field ◽  
Reconstruction Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Model Inversion

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 0.73±0.12 and 0.61±0.12 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.

scholarly journals 3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks

2021 ◽  
Vol 66 (2) ◽  
pp. 1757-1770
Author(s):  
Khalil Khan ◽  
Jehad Ali ◽  
Kashif Ahmad ◽  
Asma Gul ◽  
Ghulam Sarwar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation ◽  
Head Pose Estimation ◽  
Facial Features ◽  
Head Pose ◽  
Deep Convolutional Neural Networks ◽  
3D Head Pose Estimation

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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