Typographers who work mainly for screen display may measure their characters in pixels. Of course, you can also use standard measures, and most typography applications let you work in fractions of an inch or millimeters. A font design with a broad set width looks open while one with a narrow set width looks compact. The set width includes the width of the actual letter as well as the space needed between each character. ![]() The width of each character is called the set width. There are twelve points in a pica, and six picas equal one inch. ![]() Typographers measure the cap height of a font in units called points. The cap height is the distance from the baseline to the top of most characters. Having established the baseline, the next two considerations are height and width. A common baseline also enables typographers to combine more than one font in a document efficiently. To date, he has authored 45+ peer-reviewed publications, organizing numerous workshops on intelligent health systems and has served on the program committee of 15+ conferences, symposiums, and journals in AI and health data science.įriday, September 29 at 2:00 p.m. to 3:15 p.m.That standard line is critical because it allows typographers to align text with photos, illustrations and other media. Tafti is the 2021 SiiM Imaging Informatics Innovator awardee, Oracle for Research Project awardee, Oracle Eureka Excellence Awardee, Mayo Clinic Transform the Practice awardee, and GE Healthcare Honorable Mention awardee. He earned his BS, MS, and PhD all in Computer Science, with a main focus on fundamental and applied AI in healthcare. Furthermore, Ahmad is a Fellow of the American Medical Informatics Association, and affiliated with the Center for AI Innovation in Medical Imaging (CAIIMI), also serving as an Associate Member at UPMC Hillman Cancer Center, plus as the Vice Chair of IEEE Computer Society at Pittsburgh. He is also leading the Pitt HexAI Research Laboratory, conducting Health and Explainable AI Podcast series. Tafti is an Assistant Professor of Health Informatics in the Department of Health Information Management within the School of Health and Rehabilitation Sciences at the University of Pittsburgh, with a secondary appointment in the Intelligent Systems Program (ISP), at the School of Computing and Information. ![]() As AI-powered medical imaging continues to advance, the lessons gleaned from this study will furnish invaluable intuitions guiding the development of deep learning models toward a more inclusive, ethically sound, equitable, and unbiased healthcare landscape.Īhmad P. Through an extensive evaluation, this work offers insights into underlying causes of biases, presenting targeted mitigation strategies tailored to alleviate gender and racial biases, thereby engendering automatic segmentation results that are fair, impartial, and safe in the context of AI. The present contribution undertakes a comprehensive re-examination of deep learning-driven hip and knee bony anatomy segmentation employing plain radiographs, with a specific focus on revealing discernible gender and racial biases. Despite the remarkable performance made by deep learning computer vision algorithms in medical image segmentation, a critical concern remains largely unexplored, and that is how to uncover and address potential biases inherent within these AI-powered models. AI-powered segmentation of hip and knee bony anatomy has become indispensable in orthopedics, revolutionizing both pre-operative planning and post-operative assessment.
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