Artificial Intelligence for Breast Cancer Detection
Learn AI processing approaches for breast cancer detection, focusing on mammography and the AI processing paradigm.
Learn AI processing approaches for breast cancer detection, focusing on mammography and the AI processing paradigm.
This course provides comprehensive knowledge of artificial intelligence processing approaches for breast cancer detection. Designed for students interested in AI product development careers, it offers a unique, broad perspective on AI applications in mammography. The curriculum covers breast cancer epidemiology, breast imaging techniques, AI processing paradigms, and performance assessment metrics. Students will explore common mammographic abnormalities and learn about major AI approaches applicable to breast cancer detection, including Bayesian Neural Networks and Convolutional Neural Networks. Through quizzes, discussions, and reading assignments, students will gain a thorough understanding of the AI processing paradigm in the context of breast imaging, preparing them for entry-level jobs in the field of artificial intelligence and equipping them with knowledge for successful project implementation.
4.6
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What you'll learn
Understand breast cancer epidemiology and breast imaging techniques
Gain knowledge of AI processing paradigms and their application to mammography
Learn about performance assessment metrics for AI in medical imaging
Identify common mammographic abnormalities and their significance
Explore major AI approaches including Bayesian and Convolutional Neural Networks
Understand the application of AI techniques in breast cancer detection
Skills you'll gain
This course includes:
118 Minutes PreRecorded video
16 quizzes,12 discussion prompts
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FullTime access
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There are 4 modules in this course
This course offers a comprehensive exploration of artificial intelligence applications in breast cancer detection. It begins with an introduction to breast cancer epidemiology and breast imaging techniques, providing essential domain knowledge. Students then learn about the history and key elements of AI, including training and testing approaches, parametric and non-parametric modeling, and classification assessment metrics. The course covers common mammographic abnormalities to build a foundation for understanding AI's role in detection. Finally, it delves into specific AI approaches like Bayesian Neural Networks and Convolutional Neural Networks, demonstrating their applications in breast cancer detection. Throughout the course, students engage with quizzes, discussions, and reading assignments to reinforce their understanding of the AI processing paradigm in the context of breast imaging.
Introduction to Breast Cancer and Breast Imaging
Module 1 · 3 Hours to complete
Introduction of Artificial Intelligence
Module 2 · 3 Hours to complete
Mammographic Abnormalities
Module 3 · 3 Hours to complete
AI Applications to Breast Cancer Detection
Module 4 · 4 Hours to complete
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Instructors
Expert in Signal/Image Processing and Machine Learning
Dr. Chung-Fu Chang brings 35 years of expertise in research and development within Signal/Image Processing, Automatic Object Detection/Classification, and Synthetic Aperture Radar image formation. He has developed machine learning technologies, notably for Fingerprint Classification Systems (U.S. patent 5,572,597) and a method for orienting electronic medical images (U.S. patent 6,055,326). Since 2019, he has taught “Machine Learning for Signal Processing” at Johns Hopkins University's Whiting School of Engineering. Dr. Chang earned his B.S. in Physics from National Tsing Hua University and a Ph.D. in Biophysics from the University of California, Berkeley. He has also served as Principal Professional Staff at the Johns Hopkins University Applied Physics Laboratory and is a Life Senior Member of the IEEE Signal Processing Society.
Assistant Professor
Dr. Emily Ambinder is an Assistant Professor in the Johns Hopkins Medicine Department of Radiology and Radiological Science. She specializes in all aspects of breast imaging including mammography, tomosynthesis, breast ultrasound, breast MRI, and image-guided breast procedures. Dr. Ambinder graduated from Emory College in 2008 with a degree in Chemistry and Mathematics.
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4.6 course rating
63 ratings
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