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Artificial Intelligence for Breast Cancer Detection

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Artificial Intelligence for Breast Cancer Detection

Instructors: Chung-Fu Chang

5,625 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

81 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

81 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Breast Imaging; AI Processing Paradigm; Performance Assessment Metrics

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

16 assignments

Taught in English

91%

of learners achieved a positive career outcome

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There are 4 modules in this course

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.

Module details

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.

What's included

5 videos2 readings4 assignments4 plugins

5 videosTotal 25 minutes

Instructor Introductions and Course Overview3 minutes
Introduction to Breast Cancer and Breast Imaging6 minutes
Overview of Breast Cancer Screening6 minutes
Diagnostic Breast Cancer Imaging5 minutes
Outcome Metrics4 minutes

2 readingsTotal 20 minutes

Paper on Magnification Views10 minutes
ACR Outcome Metrics 10 minutes

4 assignmentsTotal 120 minutes

Breast Cancer and Breast Imaging Introduction30 minutes
Breast Cancer Screening30 minutes
Diagnostic Breast Imaging30 minutes
Outcome Metrics30 minutes

4 pluginsTotal 36 minutes

ACR Breast Cancer Video3 minutes
BI-RADS Summary Video3 minutes
Breast Cancer Screening Guidelines Video15 minutes
ROC Curves Explained15 minutes

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.

What's included

4 videos4 assignments4 discussion prompts

4 videosTotal 37 minutes

History of Artificial Intelligence9 minutes
AI Training and Test8 minutes
Parametric and Non-parametric Modeling7 minutes
Classification Assessment Metrics13 minutes

4 assignmentsTotal 135 minutes

History of Artificial Intelligence30 minutes
Algorithm Training and Test35 minutes
Parametric and Non-parametric Modeling35 minutes
Assessment Metrics35 minutes

4 discussion promptsTotal 40 minutes

Success and Challenges of DNN10 minutes
Processing Paradigm10 minutes
Modeling Approaches10 minutes
Feature Effectiveness Measurement10 minutes

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.

What's included

4 videos6 readings4 assignments2 plugins

4 videosTotal 14 minutes

Typically Benign Calcifications4 minutes
Suspicious Calcifications4 minutes
Benign Appearing Masses4 minutes
Suspicious Appearing Masses3 minutes

6 readingsTotal 42 minutes

Benign Calcifications Reading10 minutes
Calcification Video 1 minute
Ductal Carcinoma In Situ10 minutes
Finish Calcification Video 1 minute
Benign Breast Tumours10 minutes
Breast Cancer Reading10 minutes

4 assignmentsTotal 120 minutes

Benign Calcifications30 minutes
Suspicious Calcifications30 minutes
Benign Masses30 minutes
Suspicious Masses30 minutes

2 pluginsTotal 30 minutes

Breast Imaging: Calcifications15 minutes
Breast Imaging: Calcifications15 minutes

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.