Jan 5, 2020 · In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks ( ...
Mar 12, 2020 · In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks ( ...
In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks ( ...
The empirical study shows that the proposed approach attains superior results to all other methods, and subjective image quality assessment by human ...
Mar 19, 2020 · In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional ...
Jan 16, 2020 · In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional ...
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans. Code for training CNN for 3D medical images super resolution.
Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks.
People also ask
What is convolutional neural networks in medical imaging?
What is the convolutional neural network best for?
Which layer of a convolutional neural network is normally used to perform downsampling or dimensionality reduction?
How do convolutional neural networks work in depth?
In this paper, we study deep three-dimensional convolutional neural networks for the super-resolution of brain magnetic resonance imaging data.
In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks ( ...