The proposed processing method, PReserved Edge ConvolutIonal neural network for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulation studies and in vivo experiments to evaluate the anticipated improvements in SNR and investigate the potential for inaccuracies.
In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations.
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions.
During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task.
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
This paper develops closed-loop tactile controllers for dexterous manipulation with dual-arm robotic palms.
Robotics Systems and Control Systems and Control
The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip).