Search Results for author:
Found 8 papers, 1 papers with code
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization
We have also evaluated the proposed self-supervised generalization approach using unaccustomed containers that are far different from the ones in the training set.
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training cost and need more training data for clinical application.
Manipulation Motion Taxonomy and Coding for Robots
This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations.
Accurate Robotic Pouring for Serving Drinks
Pouring is the second most frequently executed motion in cooking scenarios.
Learning to Pour
We present a pouring trajectory generation approach, which uses force feedback from the cup to determine the future velocity of pouring.
