This course provides attendees with strategies and ideas for navigating through the early years of Medical Imaging research in the academic environment. The course focuses on strategic career planning topics such as effective CV development, understanding the Promotion & Tenure process, resource negotiating tips, time management & organizational skills, and writing and winning research grants.

Artificial Intelligence (AI) is increasingly being used in a wide variety of medical imaging applications. Most of the focus, however, is on algorithm and scheme development, but this is only part of the picture. In order to have an impact on clinical decision making, workflow and patient care these AI tools must be evaluated using real-world cases and actual clinical providers that are expected to use them in routine care. The techniques used to conduct these types of studies are less well known in this field thus investigators need to be trained the proper study design and analysis methods. This course will cover basic principles, techniques, and process for validating models developed using artificial intelligence (AI)/machine learning (ML) techniques. The primary goal of this course is to help the audience understand and apply fundamental principles related to designing, executing, and interpreting model evaluation studies. The course will be organized around two parts: analytic validation and clinical validation. In the first half, the audience will be exposed to approaches for performing a technical validation of a prediction model, including different study designs, appropriate statistical tests, metrics, dataset considerations, and decision curve analysis. The second half will cover the process of undertaking clinical validation that would address real-world use of models, regulatory and deployment issues. Topics include workflow integration, prospective clinical trials, reader impact studies, and regulatory approvals. Examples will focus on imaging-related models that are drawn from literature and the instructors’ personal experiences in prognostic modeling, computer-aided diagnosis, and imaging biomarker development.

This full-day course will provide attendees with the statistical tools needed for current bioengineering and medical imaging research. It will describe observer performance data acquisition strategies and their analyses. Observer performance methods are a set of highly evolved tools that are loosely referred to as "ROC analyses", but which now encompass two-alternative forced choice (2AFC), receiver operating characteristic (ROC), location ROC (LROC), region of interest (ROI) and free-response ROC (FROC) methods. They are used to assess radiologist performance, to perform technology comparison studies, for image quality assessment, and to assess CAD algorithms developed for mammography and lung cancer screening.

This course provides you with a basic working knowledge of color and color calibration. The course concentrates on consistent color and consistent grayscale presentation for the increasingly used color displays in medical imaging replacing the traditional monochrome displays. There will be four main subject areas: • The science of color and color measurements. Here you will learn about the tri-chromatic theory, the color-matching functions, the tristimulus values, the chromaticity coordinates and the various color spaces. Of particular importance for the measurement of color are the colorimeter and the spectro-photometer. • Computational tools for calibration and color reproduction. In this section you will be exposed to a typical system consisting of a colorimeter and a color display with an image for measuring color coordinates of that image, or a color camera for capturing the image of a color display like a color LCD. You learn that you need to do an input calibration, processing and an output calibration. • Assessment of display performance for medical imaging systems. In this section you will be reminded of the AAPM Task Group 18 and its efforts to establish performance criteria recommending minimum performance requirements for safe utilization of electronic monochrome displays in medical applications. You will learn that such efforts will also be required for color displays. • Visual thresholds and the effect of color on the correct diagnosis. In this section you will hear about some of the areas in medical imaging in which the importance of color images and color displays have been explored. Emphasis will be on radiologic imaging, but examples from other areas such as pathology and dermatology where true color images are used will also be referenced. Ultimately the objective of this course is to familiarize you with the elements of consistent color presentation as well as consistent grayscale presentation of medical images on digital color displays for medical image interpretation.