Resources for the Advancement of Women in Medical Imaging Informatics

More Women Join ACR Leadership, But Rates Still Lag

Over the past 15 years, the number of women holding leadership positions in the American College of Radiology (ACR) has significantly increased at the state level and in terms of fellowship recognition, but has still lagged nationally.

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Survey: Rad residency programs must sharpen efforts to draw women, engage med students

When it comes to radiology, both men and women in medical school are more likely to pursue a field they have had positive exposure to. However, women frequently rank radiology less positively than men. How can we change this perception?

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Information Technology Case Studies

A series of case studies from the American College of Radiology. Radiology has always been at the forefront of medical technology. Imaging 3.0 encourages radiologists to continue pushing the technological envelope in ways that improve patient care.

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Machine Learning Course 2

This online course from Stanford University provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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Machine Learning Course 1

A fantastic suggested course from a fellow RADxxer This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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Strategies for Improving Reporting in Radiology

Radiology reports are a critical part of the role of the radiologist in patient care. However, they often confuse and elude patients. This study determines techniques that can be employed to make radiology reporters from useful, actionable, and patient friendly.

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