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Johns Hopkins University

Artificial Intelligence for Breast Cancer Detection

Johns Hopkins University via Coursera

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Overview

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The objective of this course is to provide students the knowledge of artificial intelligence processing approaches to breast cancer detection. Students will take quizzes and participate in discussion sessions to reinforce critical concepts conveyed in the modules. Reading assignments, including journal papers to understand the topics in the modules, will be provided. The course is designed for students who are interested in the career of product development using artificial intelligence and would like to know how AI can be applied to mammography. The course content is focused on the AI processing paradigm along with the domain knowledge of breast imaging. This course approach is unique, providing students a broad perspective of AI, rather than homing in on a particular implementation method. Students who complete this course will not only leverage the knowledge into an entry level job in the field of artificial intelligence but also perform well on projects because their thorough understanding of the AI processing paradigm.

Syllabus

  • Introduction to Breast Cancer and Breast Imaging
    • This module introduces the fundamentals of breast cancer epidemiology and the role of imaging in breast cancer detection. You will examine current screening recommendations, understand the difference between screening and diagnostic mammography, and explore key imaging techniques used in clinical practice. The module also reviews measurable outcome metrics used to evaluate performance in breast imaging.
  • Introduction of Artificial Intelligence
    • This module introduces the fundamental concepts and technologies behind artificial intelligence. You will explore the history of AI, understand how models are trained and tested, and examine the differences between parametric and non-parametric approaches. The module also explains how classification performance is evaluated using standard AI assessment metrics.
  • Mammographic Abnormalities
    • This module examines common abnormalities identified in mammographic imaging. You will learn to distinguish between benign and malignant characteristics of calcifications and masses on mammography. Understanding these imaging features provides the clinical foundation for applying artificial intelligence to breast cancer detection.
  • AI Applications to Breast Cancer Detection
    • This module explores how artificial intelligence techniques are applied to breast cancer detection. You will examine different AI approaches, including Bayesian models and deep learning neural networks, and understand how classifiers are developed for medical imaging tasks. The module also highlights current research directions and emerging applications of AI in breast imaging.

Taught by

Emily B Ambinder and Chung-Fu Chang

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Reviews

4.0 rating, based on 1 Class Central review

4.6 rating at Coursera based on 81 ratings

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  • Profile image for Sonal P Patel
    Sonal P Patel @sonal
    53
    This class is most suitable for radiologists and AI developers interested in developing AI-based solutions to read screening mammograms. Currently, mammograms are the most popular method for screening for breast cancer and early diagnosis is the…
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