2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) 2018
DOI: 10.1109/icmla.2018.00083
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Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble based 3D Densely Connected Convolutional Networks

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Cited by 52 publications
(14 citation statements)
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“…Besides 2D CNN architectures, 3D convolutional layers enabled scientists to incorporate the volumetric data into the training process, and such approaches produced promising predictions using structural MRI data [55]- [57]. The 3D models used the signal intensity at the voxel level and applied the convolution operator to 3D filters and previous-layer feature maps.…”
Section: Related-workmentioning
confidence: 99%
“…Besides 2D CNN architectures, 3D convolutional layers enabled scientists to incorporate the volumetric data into the training process, and such approaches produced promising predictions using structural MRI data [55]- [57]. The 3D models used the signal intensity at the voxel level and applied the convolution operator to 3D filters and previous-layer feature maps.…”
Section: Related-workmentioning
confidence: 99%
“…Recently, deep learning technology has been popularized in medical image processing, and has been applied in maturity recognition [5], [6], disease analysis [7], cross-modal data supplement [8] and other fields. Many deep learning reconstruction models, such as Generative adversarial networks (GANs) and auto encoders (AEs), are widely used in reconstructing 2D images.…”
Section: Related Workmentioning
confidence: 99%
“…However, the details regarding CV methodology and classification decisions are not presented in this study. Wang et al [33] proposed an ensemble of 3D densely connected convolutional networks (3D-DenseNets) for three-class AD, MCI, and HC diagnosis. In their model, MRI scans of the same patients that are over three years apart are employed as different samples, incorporating information of test data into the learning process.…”
Section: Related Workmentioning
confidence: 99%