We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.

Imagine your body is like a loaf of sliced bread. During an MRI scan, a powerful magnet and radio waves create detailed images of each "slice" of your body, then a computer puts the slices together to show a full picture of your anatomy. But before the slicing comes the choosing. Before an MRI technologist can scan a patient, they have to manually specify the slices they want the MRI to acquire. This process can take several minutes of tweaking and adjusting, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan.

Posted by Jason A. Polzin, PhD GM Applications and Workflow, GE Healthcare Global Magnetic Resonance Imaging Here we describe our experience using TensorFlow to train a neural network to identify specific anatomy during a brain magnetic resonance imaging (MRI) exam to help improve speed and consistency. MRI (Figure 1.) is a 3D imaging technique that allows clinicians to visualize structures in the body non-invasively and without ionizing radiation. MRI is a widely used and powerful imaging modality due to its superior contrast between "soft" tissues, e.g. One of the key strengths of MRI is being able to image specific locations in the body at an orientation best suited for the purpose of the exam. This means that the operator must plan these scans carefully to yield the best possible images uniquely oriented for each patient to visualize the specific structures that may be of interest.