Artificial Intelligence for Breast Cancer Detection

Artificial Intelligence for Breast Cancer Detection

Instructors: Chung-Fu Chang

5,446 already enrolled

Included with

Learn more

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

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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 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

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 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

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 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

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 videosTotal 44 minutes

Building Classifier8 minutes
Bayesian Neural Network16 minutes
Convolutional Neural Network11 minutes
Applications to Breast Cancer Detection10 minutes

1 readingTotal 35 minutes

Bayesian Neural Network 35 minutes

4 assignmentsTotal 135 minutes

Building Classifier30 minutes
Bayesian Neural Network35 minutes
Convolutional Neural Network35 minutes
Applications to Breast Cancer Detection35 minutes

4 discussion promptsTotal 40 minutes

Value-added Tool10 minutes
Innovation of BNN10 minutes
Innovation of CNN10 minutes
Future AI Applications10 minutes

Instructors

Instructor ratings

(29 ratings)

Chung-Fu Chang

Johns Hopkins University

1 Course5,446 learners

Offered by

Johns Hopkins University

Explore more from Machine Learning

Coursera
Breast Cancer Prediction Using Machine Learning
Guided Project
Microsoft
Machine Learning and AI Applications in Healthcare
Course
DeepLearning.AI
AI for Medical Diagnosis
Course
IBM
Introduction to Computer Vision and Image Processing
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
77.77%
4 stars
11.11%
3 stars
6.17%
2 stars
1.23%
1 star
3.70%

Showing 3 of 81

Reviewed on May 18, 2023

It was a very good experience. I am glad I opted for this.

Reviewed on Jun 14, 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 11, 2022

it has been really helpful and informative for me.

View more reviews

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.