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Imaging informatics, also known as radiology informatics or medical imaging informatics, is a subspecialty of biomedical informatics that aims to improve the efficiency, accuracy, usability and reliability of medical imaging services within the healthcare enterprise. [1]
In the United States, there are 352 accredited health information management (HIM) and health informatics (HI) programs. Most of these programs are in the associate degree level. In Canada, there are 19 different HIM programs, mostly in the diploma level.
The program is housed in the School of Computing in Shelby Hall, a recently completed $50 million state of the art teaching facility. The University of South Alabama awarded David L. Loeser on May 10, 2014, with the first Health Informatics degree. The program currently is scheduled to have 100+ students awarded by 2016.
In the United States, health information managers are typically certified as a Registered Health Information Administrator (RHIA) after achieving a bachelor's degree in health informatics or health information management from a school accredited by the Commission on Accreditation for Health Informatics and Information Management Education ...
Ron Kikinis is an American physician and scientist best known for his research in the fields of imaging informatics, image guided surgery, and medical image computing.He is a professor of radiology at Harvard Medical School.
The School of Informatics was founded in 2000 as the first school of its kind in the United States. That fall, the first classes were offered on both campuses. Two years later, in 2002, the school hired its first full professor (Bill Aspray) and conferred its first degrees (22 students).
Segmentation is the process of partitioning an image into different meaningful segments. In medical imaging, these segments often correspond to different tissue classes, organs, pathologies, or other biologically relevant structures. [2] Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities.
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data.
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