Resources for the Advancement of Women in Medical Imaging Informatics

Is the Future of Radiology Diversity, Acceptance, and Mutual Understanding?

Hear from one of our #RADxx members, Amy Patel, MD, breast radiologist on the American College of Radiology’s blog. According to Dr. Patel, “We as radiologists will be serving a diverse patient population. We will need to be prepared to meet the needs of our patients, including arriving to mutual understanding to better serve them.”

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Takeaways from the 2017 ACR Commission on Human Resources Workforce Survey

One of the key takeaways of the 2017 ACR Commission on Human Resource Survey was that gender distribution remains the same; the radiology field is still primarily male. However, the divide is a bit less in younger age groups. And most importantly, only 13% of practice leaders are female. How can we encourage more women […]

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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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