Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency.
To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types.
The primary goal of skeletal motion prediction is to generate future motion by observing a sequence of 3D skeletons.
In the reported approach, we project illumination patterns at a tilted angle with respect to the detection optics.
Here we model the Fourier ptychographic forward imaging process using a convolution neural network (CNN) and recover the complex object information in the network training process.
For each focus point on the map, sample needs to be static in the x-y plane and axial scanning is needed to maximize the contrast.