Search Results for author:
Found 7 papers, 3 papers with code
Improving the Performance of Object Detection by Preserving Balanced Class Distribution
We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on private datasets that may have imbalanced class distribution.
Improving the performance of object detection by preserving label distribution
We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution.
Deep learning-based reconstruction of highly accelerated 3D MRI
The trained model was evaluated on 3D MPRAGE brain scan data retrospectively-undersampled with a 10-fold acceleration, compared to a conventional parallel imaging method with a 2-fold acceleration.
Artificial Intelligence in PET: an Industry Perspective
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications.
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
Generative Adversarial Network
Image-to-Image Translation
+2
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan.
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
