Artificial Intelligence for Breast Cancer Detection

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Artificial Intelligence for Breast Cancer Detection

Instructors: Chung-Fu Chang

5,428 already enrolled

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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

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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.

In module 1, you will be introduced to breast cancer epidemiology and approaches to breast cancer imaging.

What's included

5 videos2 readings4 assignments4 plugins

5 videos Total 25 minutes

Instructor Introductions and Course Overview 3 minutes
Introduction to Breast Cancer and Breast Imaging 6 minutes
Overview of Breast Cancer Screening 6 minutes
Diagnostic Breast Cancer Imaging 5 minutes
Outcome Metrics 4 minutes

2 readings Total 20 minutes

Paper on Magnification Views 10 minutes
ACR Outcome Metrics 10 minutes

4 assignments Total 120 minutes

Breast Cancer and Breast Imaging Introduction 30 minutes
Breast Cancer Screening 30 minutes
Diagnostic Breast Imaging 30 minutes
Outcome Metrics 30 minutes

4 plugins Total 36 minutes

ACR Breast Cancer Video 3 minutes
BI-RADS Summary Video 3 minutes
Breast Cancer Screening Guidelines Video 15 minutes
ROC Curves Explained 15 minutes

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

What's included

4 videos4 assignments4 discussion prompts

4 videos Total 37 minutes

History of Artificial Intelligence 9 minutes
AI Training and Test 8 minutes
Parametric and Non-parametric Modeling 7 minutes
Classification Assessment Metrics 13 minutes

4 assignments Total 135 minutes

History of Artificial Intelligence 30 minutes
Algorithm Training and Test 35 minutes
Parametric and Non-parametric Modeling 35 minutes
Assessment Metrics 35 minutes

4 discussion prompts Total 40 minutes

Success and Challenges of DNN 10 minutes
Processing Paradigm 10 minutes
Modeling Approaches 10 minutes
Feature Effectiveness Measurement 10 minutes

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.

What's included

4 videos6 readings4 assignments2 plugins

4 videos Total 14 minutes

Typically Benign Calcifications 4 minutes
Suspicious Calcifications 4 minutes
Benign Appearing Masses 4 minutes
Suspicious Appearing Masses 3 minutes

6 readings Total 42 minutes

Benign Calcifications Reading 10 minutes
Calcification Video 1 minute
Ductal Carcinoma In Situ 10 minutes
Finish Calcification Video 1 minute
Benign Breast Tumours 10 minutes
Breast Cancer Reading 10 minutes

4 assignments Total 120 minutes

Benign Calcifications 30 minutes
Suspicious Calcifications 30 minutes
Benign Masses 30 minutes
Suspicious Masses 30 minutes

2 plugins Total 30 minutes

Breast Imaging: Calcifications 15 minutes
Breast Imaging: Calcifications 15 minutes

In this module, we will explore two major AI approaches which are applicable to the breast cancer detection.

What's included

4 videos1 reading4 assignments4 discussion prompts

4 videos Total 44 minutes

Building Classifier 8 minutes
Bayesian Neural Network 16 minutes
Convolutional Neural Network 11 minutes
Applications to Breast Cancer Detection 10 minutes

1 reading Total 35 minutes

Bayesian Neural Network 35 minutes

4 assignments Total 135 minutes

Building Classifier 30 minutes
Bayesian Neural Network 35 minutes
Convolutional Neural Network 35 minutes
Applications to Breast Cancer Detection 35 minutes

4 discussion prompts Total 40 minutes

Value-added Tool 10 minutes
Innovation of BNN 10 minutes
Innovation of CNN 10 minutes
Future AI Applications 10 minutes

Instructors

Instructor ratings

(29 ratings)

Chung-Fu Chang

Johns Hopkins University

1 Course 5,428 learners

Offered by

Johns Hopkins University

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Reviewed on Oct 10, 2022

it has been really helpful and informative for me.

Reviewed on Jun 13, 2023

It was extremely detailed and gave me a thorough understanding of the topic. I hope to have more courses like this in the future.

Reviewed on Oct 3, 2021

The session was indeed informative and as best as it could've been. I am glad to be a participant and look forward to learn.

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