Abstract: The field of imaging informatics is rapidly advancing in its ability to address challenges related to clinical big data and harnessing this information for precision medicine. In the past year, the field has experienced growth in a variety of areas including machine learning (learning from imaging data for diagnostic and prognostic predictions), radiomics (the generation of high-dimensional features from images), patient-oriented sharing and communication of images and image-derived findings, and the growth of the use of imaging beyond radiology. Novel approaches go beyond pixel data to integrate imaging with other biomedical data, standardize imaging workflows, and improve the quality and utility of image-derived information in clinical practice. In this session, we will review key advances in imaging informatics research published this past year.
Learning Objective 1: Summarize the current trends and late-breaking topics in the area of imaging informatics and its relation to biomedical informatics
Learning Objective 2: Recognize how developments in imaging informatics can help inform solutions to broader challenges in biomedical informatics
William Hsu (Presenter)
University of California, Los Angeles
Charles Kahn (Presenter)
University of Pennsylvania