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Johns Hopkins University

Artificial Intelligence for Breast Cancer Detection

Johns Hopkins University via Coursera

Overview

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 re-enforce 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 the product development using artificial intelligence and would like to know how AI can be applied to the mammography. The course information 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 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 the projects because their thorough understanding of the AI processing paradigm.

Syllabus

  • Introduction to Breast Cancer and Breast Imaging
    • In module 1, you will be introduced to breast cancer epidemiology and approaches to breast cancer imaging.
  • Introduction of Artificial Intelligence
    • In Module 2, we will introduce the history of AI and the key elements and approaches. We will also define the assessment methods of AI classification performance
  • Mammographic Abnormalities
    • In this module, we will review common abnormalities identified on breast imaging in order to pave the way to thinking about using AI in detection.
  • AI Applications to Breast Cancer Detection
    • In this module, we will explore two major AI approaches which are applicable to the breast cancer detection.

Taught by

Emily B Ambinder and Chung-Fu Chang

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