Creating Training Sets Quickly and Easily For Computer Vision Applications for the Healthcare Sector
Computer vision sits at the forefront of improving the healthcare sector with neural network models already identifying and analyzing medical images from many sources. To develop these models, scientists and researchers must train these systems to identify and aggregate a large quantity of medical data, and the scale of data that scientists need to work with is enormous and ever-growing. Holding back the development of computer vision applications is the tedious and cumbersome process associated with creating training sets, that is, collecting, preparing and managing large, image datasets. In many cases, data management takes up more time than training. One of the biggest challenges in computer vision, therefore, is the current inefficiencies regarding data collection, preparation and management. Imagine a platform that provides you with standardized versions of large, annotated medical datasets so you no longer have to waste time with converting large files into one single format. A platform that provides you with a series of flexible tools that normalize the data and allows you to search, filter, browse with no requirement for serious hardware capabilities. A platform that enables you to transform your raw datasets to augmented and preprocessed training sets quickly and easily. I will discuss the current challenges facing researchers and scientists with managing large image datasets and the ways in which CVEDIA is helping data scientists simplify the data management process
A graduate from the London School of Economics, Natalia's professional experience includes over 10 years writing and research for a variety of global clients including: think tanks in the US, Canada and Israel; intergovernmental organizations including the United Nations Coordination for Humanitarian Affairs; PR and advertising firms; financial services firms; and startups focusing on hi-tech. Having been invited to join CVEDIA, she is on a steep learning curve and is humbled to work alongside a team of incredibly forward-thinking, technical geniuses.