Understand basic premise of machine learning
Understand terminology specific for machine learning
Apply basic programming skills to create their owndeep learning algorithm
Machine learning is not a new concept and has been present since the 1950s. It has, however, seen an exponential increase in interest and application in the past 10 years partly due to the increased computer processing power available. This increase in popularity is not just in the computer science field, but has spilled over into the radiology realm, with the recent creation of the Radiology: Artificial Intelligence journal. While there are many reference articles available, a step-by-step how-to guide is lacking.
The premise of...
Imaging findings OR Procedure details
Tensorflow1 is an open source platform that allows ease of creating your own machine learning algorithm. Instead of dealing with linear algebra and writing each layer by individually, you can simply specify the number of nodes, layers and activation function. In the following section, we will demonstrate how to retrain a machine learning algorithm for a new classification task.
The following instructions are for Ubuntu and a Nvidia graphics card. Install Nvidia driver, Docker (https://docs.docker.com/install/) and Nvidia docker (https://github.com/NVIDIA/nvidia-docker). Pull the tensorflow docker...
Machine learning in radiology is probably at it peak at on the Gartner hype cycle, with numerous research articles written and commercial software being developed at this time. In order to allow everyone some understanding behind this process, we have introduced the basic concept underlying deep learning and illustrated a brief way to get started on any deep learning project.
David Wang is currently a nuclear medicine fellow at the University of Cincinnati Medical Center (UCMC), Cincinnati, OH after having completed a neuroradiology fellowship at UCMC. He trainedat the Royal Melbourne Hospitaland was admitted as a fellow of RANZCR in 2018.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E.,Chen,Z.,Citro,C., et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.