To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

The pressures facing healthcare delivery organizations are well documented: increased regulatory oversight, financial challenges, heightened competition, a shrinking talent pool, and many more. Also well known is the army of consultants, systems integrators and technology companies promising their solutions will address one or more of these market trends. All too often, implementation fails to deliver the promised results, leaving the healthcare organization with a big bill and little to show for it. What if there were new solutions, built on the powerful capabilities of artificial intelligence and machine learning (AI/ML) that provided proven, quantifiable cost savings of 30% or more, and consistent improvements in uptime for some of the most expensive equipment healthcare facilities operate? Harbor Research, a leading strategy and technology research firm, notes utilizing AI/ML to leverage complex machine data from healthcare imaging equipment alone will provide $11.1 billion in revenue value (decreased costs/increased revenue generation) by 2022.