ELIC Publishes Paper Showing Open Source System Supports Deep Learning AI and Quantitative Imaging Analyses on Cloud-Based Datasets

The International Association for the Study of Lung Cancer (IASLC), Accumetra, and numerous international CT lung cancer screening researchers published a paper in the Journal of Thoracic Oncology (JTO) showing that the open source Early Lung Imaging Confederation (ELIC) system can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets. This paper provides results from deep learning and quantitative imaging experiments performed on 697 lung cancer screening cases with two CT image acquisition time points each. ELIC is designed to support the computational study of large collections of early lung cancer cases, where image data stays on the cloud within local regions while AI and quantitative imaging algorithms are distributed to those data locations. To read more about how this new global infrastructure can advance medical AI visit: