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Chung-Fu Chang
and
Emily Ambinder
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/sgqr4m/artificial
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What's inside
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.
Read more
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines artificial intelligence (AI) in breast cancer detection, which is highly relevant to healthcare professionals and researchers in radiology and oncology
Develops an understanding of AI processing approaches specifically for breast cancer detection, which is useful for both medical professionals and AI practitioners
Taught by instructors Chung-Fu Chang and Emily Ambinder, who are recognized for their work in AI and breast cancer research
Provides a comprehensive study of AI applications in breast cancer detection, including both theoretical foundations and practical considerations
Explicitly requires students to have some background knowledge in AI and breast imaging
Does not offer hands-on labs or interactive materials, which might be preferred by some learners
Save this course
Save Artificial Intelligence for Breast Cancer Detection to your list so you can find it easily later:
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Reviews summary
Breast cancer detection with ai
According to students, Artificial Intelligence for Breast Cancer Detection is an informative course that helps learners develop a strong understanding of the fundamentals of AI in medicine. Many learners enjoyed the thorough and engaging content, while others found that it could benefit from a more beginner-friendly approach or deeper exploration of certain topics.
Course helps learners understand how AI relates to breast cancer detection.
"I could understand how artificial intelligence works and how it relates to breast cancer screening."
"I was able to learn that artificial intelligence is indispensable for medical care."
Very informative, learners found it engaging.
"it has been really helpful and informative for me."
"The session was indeed informative and as best as it could've been."
"It was extremely detailed and gave me a thorough understanding of the topic."
Mixed opinions on depth, some learners suggest more beginner-friendly content while others would like deeper exploration of topics.
"The course was vague and not beginner friendly."
"Breast cancer part of the course was amazingly dealt with, while the AI part of it could do with some depth and clarity."
"Impractical course! I recommend changing the title of the course to "Artificial Intelligence for Breast Cancer Detection: THEORY."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Artificial Intelligence for Breast Cancer Detection with these
activities:
Review Course Materials
Show steps
Organize and review notes, assignments, and other course materials to solidify understanding of the concepts covered.
Show steps
-
Review lecture notes.
-
Revisit assignment solutions.
-
Complete practice problems or quizzes.
Review: Medical Image Analysis
Show steps
Read and review a textbook on medical image analysis to gain a deeper understanding of the field
View
Biomedical Engineering e-Mega Reference
on Amazon
Show steps
-
Read the introduction and first chapter.
-
Review the chapters on image segmentation and registration.
-
Complete the practice exercises provided in the book.
Statistics Review
Show steps
Review statistical concepts to strengthen foundational knowledge of statistical techniques used in the course.
Browse courses on
Statistical Concepts
Show steps
-
Review probability and distributions.
-
Practice calculating descriptive statistics.
-
Review hypothesis testing concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding Session
Show steps
Collaborate with peers to practice coding skills and reinforce concepts covered in the course.
Browse courses on
Python
Show steps
-
Meet with a peer and discuss a coding challenge.
-
Work together to develop a solution.
-
Review and refine the code.
Machine Learning Tutorial
Show steps
Explore tutorials on machine learning to gain a deeper understanding of the concepts and techniques used in the course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find tutorials on supervised learning algorithms.
-
Follow tutorials on unsupervised learning algorithms.
-
Practice implementing machine learning models.
Data Visualization
Show steps
Create data visualizations to present and interpret data effectively, reinforcing analytical skills.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset to visualize.
-
Select appropriate charts and graphs to represent the data.
-
Create the visualizations and analyze the results.
Attend Industry Conference
Show steps
Attend industry conferences to connect with professionals in the medical imaging field, gain insights, and expand professional network.
Show steps
-
Research upcoming conferences related to medical imaging.
-
Register for a conference and prepare talking points.
-
Attend sessions, network with attendees, and gather information.
Consult with Experts
Show steps
Seek guidance from professionals in the field to gain insights, explore career paths, and receive industry-specific advice.
Browse courses on
Breast Cancer
Show steps
-
Identify experts in the field of breast cancer detection.
-
Reach out and schedule consultations or informational interviews.
-
Prepare questions and actively engage in discussions.
Review Course Materials
Show steps
Organize and review notes, assignments, and other course materials to solidify understanding of the concepts covered.
Show steps
Show steps
Show steps
- Review lecture notes.
- Revisit assignment solutions.
- Complete practice problems or quizzes.
Review: Medical Image Analysis
Show steps
Read and review a textbook on medical image analysis to gain a deeper understanding of the field
View
Biomedical Engineering e-Mega Reference
on Amazon
Show steps
Show steps
Show steps
- Read the introduction and first chapter.
- Review the chapters on image segmentation and registration.
- Complete the practice exercises provided in the book.
Statistics Review
Show steps
Review statistical concepts to strengthen foundational knowledge of statistical techniques used in the course.
Browse courses on
Statistical Concepts
Show steps
Show steps
Show steps
- Review probability and distributions.
- Practice calculating descriptive statistics.
- Review hypothesis testing concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Coding Session
Show steps
Collaborate with peers to practice coding skills and reinforce concepts covered in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Meet with a peer and discuss a coding challenge.
- Work together to develop a solution.
- Review and refine the code.
Machine Learning Tutorial
Show steps
Explore tutorials on machine learning to gain a deeper understanding of the concepts and techniques used in the course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find tutorials on supervised learning algorithms.
- Follow tutorials on unsupervised learning algorithms.
- Practice implementing machine learning models.
Data Visualization
Show steps
Create data visualizations to present and interpret data effectively, reinforcing analytical skills.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset to visualize.
- Select appropriate charts and graphs to represent the data.
- Create the visualizations and analyze the results.
Attend Industry Conference
Show steps
Attend industry conferences to connect with professionals in the medical imaging field, gain insights, and expand professional network.
Show steps
Show steps
Show steps
- Research upcoming conferences related to medical imaging.
- Register for a conference and prepare talking points.
- Attend sessions, network with attendees, and gather information.
Consult with Experts
Show steps
Seek guidance from professionals in the field to gain insights, explore career paths, and receive industry-specific advice.
Browse courses on
Breast Cancer
Show steps
Show steps
Show steps
- Identify experts in the field of breast cancer detection.
- Reach out and schedule consultations or informational interviews.
- Prepare questions and actively engage in discussions.
Career center
Learners who complete Artificial Intelligence for Breast Cancer Detection will develop knowledge and skills
that may be useful to these careers:
Radiologist
Radiologist
Radiologists are medical doctors who specialize in using imaging technologies to diagnose and treat diseases. This course may be useful to Radiologists who are interested in exploring the use of AI in breast cancer detection and want a deeper understanding of its applications.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants advise healthcare organizations on how to improve their operations and quality of care. This course may be helpful to Healthcare Consultants who want to learn more about AI in breast cancer detection and how it can be used to improve patient outcomes.
Medical Researcher
Medical Researcher
Medical Researchers conduct research to understand the causes, treatments, and preventions of diseases. This course may prove useful to Medical Researchers interested in understanding the role of AI in breast cancer detection and how it can be used to improve patient outcomes.
Product Manager
Product Manager
Product Managers are responsible for the development, launch, and marketing of products. This course may be beneficial to Product Managers who are looking to develop AI-powered products in the healthcare industry, particularly those focused on breast cancer detection.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers analyze and interpret the needs of various stakeholders to develop comprehensive AI solutions. Coursework will introduce you to AI and teach you how to apply its elements and approaches to breast cancer detection. Your studies will also provide a foundation in using AI as applied to a particular domain, in this case, mammography.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course will provide you with a strong foundation in AI and its applications in breast cancer detection. You will also gain practical experience in using AI processing paradigms, which will be valuable for Machine Learning Engineers working on AI-powered breast cancer detection solutions.
Data Scientist
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract insights and knowledge from data in various forms, both structured and unstructured. This course will aid prospective Data Scientists by delving into one application of AI, using it for breast cancer detection. This can help build a foundation in using AI to solve problems in one specific medical domain. Moreover, students will get a practical understanding of the assessment methods of AI and will learn to evaluate AI classification performance, skills that are in great demand in this role.
Software Engineer
Software Engineer
Software Engineers analyze user needs and develop and design software prototypes, applications, and other digital products. This course will help you to develop a stronger understanding of the applications of AI in breast cancer detection. You will also learn about AI processing paradigms, which will be helpful for Software Engineers working on AI-powered breast cancer detection solutions.
Data Analyst
Data Analyst
Data Analysts collect, clean, analyze, and interpret data in order to help businesses make informed decisions. This course will provide a solid foundation in AI and its applications in breast cancer detection, enabling you to apply data analysis skills in this specific medical domain.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. This course will provide you with a solid foundation in AI and its applications in breast cancer detection, which can be beneficial for Quantitative Analysts looking to specialize in the healthcare industry.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers apply engineering principles to medical problems. This course will introduce you to AI and its applications in breast cancer detection. It will also enable you to gain practical knowledge in the assessment methods of AI and how to evaluate AI classification performance.
Medical Physicist
Medical Physicist
Medical Physicists apply the principles of physics to medicine. This course will introduce you to AI and its applications in breast cancer detection. Given the increasing use of AI in medical imaging, this knowledge can be beneficial to Medical Physicists looking to stay current with the latest advances in their field.
Health Educator
Health Educator
Health Educators promote healthy behaviors and lifestyles through education. This course will provide you with a stronger understanding of AI in breast cancer detection, a topic you may encounter while providing information and education about breast cancer screening.
Doctor
Doctor
Doctors provide medical care to patients. Upon completing this course, you will have a better understanding of the role of AI in breast cancer detection, which may prove useful given its increasing use as a diagnostic tool.
Nurse
Nurse
Nurses provide medical care to patients. Upon completing this course, you will have a better understanding of the role of AI in breast cancer detection, which may prove useful given its increasing use as a diagnostic tool.
For more career information including salaries, visit:
OpenCourser.com/course/sgqr4m/artificial
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Artificial Intelligence for Breast Cancer Detection.
Comprehensive guide to breast imaging, covering all aspects of breast cancer detection, diagnosis, and treatment. It valuable resource for radiologists, oncologists, and other healthcare professionals involved in the care of breast cancer patients.
Save
Provides a comprehensive overview of the applications of AI in medicine, including breast cancer detection. It covers the ethical, legal, and social implications of AI in healthcare, as well as the future of AI in medicine.
Comprehensive guide to breast cancer screening and diagnosis. It covers all aspects of breast cancer detection, from risk assessment to diagnosis and treatment.
Save
Comprehensive guide to breast cancer for patients and families. It covers all aspects of breast cancer, from diagnosis and treatment to survivorship and palliative care.
Comprehensive guide to breast cancer for patients, families, and friends. It covers all aspects of breast cancer, from diagnosis and treatment to survivorship and palliative care.
Comprehensive guide to breast cancer for young women. It covers all aspects of breast cancer, from diagnosis and treatment to survivorship and palliative care.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sgqr4m/artificial
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Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructors
Chung-Fu Chang
Emily Ambinder
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
Good to know
Examines artificial intelligence (AI) in breast cancer detection, which is highly relevant to healthcare professionals and researchers in radiology and oncology
Develops an understanding of AI processing approaches specifically for breast cancer detection, which is useful for both medical professionals and AI practitioners
Taught by instructors Chung-Fu Chang and Emily Ambinder, who are recognized for their work in AI and breast cancer research
Provides a comprehensive study of AI applications in breast cancer detection, including both theoretical foundations and practical considerations
Explicitly requires students to have some background knowledge in AI and breast imaging
Does not offer hands-on labs or interactive materials, which might be preferred by some learners
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