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Sebastian Thrun,
Cezanne Camacho,
Jay Alammar,
Alexis Cook,
Luis Serrano,
Juan Delgado,
and
Ortal Arel
Learn how to utilize neural networks to distinguish between images of benign and cancerous skin tissue.
Register for this course and see more details by visiting:
OpenCourser.com/course/1w9koj/skin
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What's inside
Syllabus
Sebastian Thrun teaches us about his groundbreaking work detecting skin cancer with convolutional neural networks.
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Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Sebastian Thrun who is noteworthy for their work in detecting skin cancer with neural networks
Provides a foundation for students interested in learning about convolutional neural networks and their applications in the medical field
Utilized by professionals in healthcare field, like dermatologists, to improve the early detection of skin cancer through the use of neural networks
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Save Skin Cancer Detection to your list so you can find it easily later:
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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 Skin Cancer Detection with these
activities:
Review neural network fundamentals
Show steps
Reinforce your understanding of the fundamental concepts of neural networks, such as forward and backward propagation, to better grasp the course content.
Browse courses on
Convolutional Neural Networks
Show steps
-
Revisit online tutorials on neural network basics
-
Review textbook chapters on neural network architectures
-
Solve practice problems on neural network training
Follow tutorials on convolutional neural networks (CNNs)
Show steps
Delve deeper into the specific architecture of CNNs to better comprehend their application in the course.
Browse courses on
Convolutional Neural Networks
Show steps
-
Explore online tutorials on CNN architecture
-
Complete hands-on exercises on CNN implementation
-
Experiment with different CNN hyperparameters
Practice identifying benign and cancerous skin lesions
Show steps
Sharpen your ability to visually distinguish between benign and cancerous skin lesions, a key skill for the course.
Show steps
-
Examine online databases of skin lesion images
-
Participate in online forums for dermatology enthusiasts
-
Consult with a dermatologist to gain expert insights
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a visual representation of the CNN architecture
Show steps
Enhance your understanding of CNN architecture by creating a visual representation, such as a diagram or infographic.
Browse courses on
Convolutional Neural Networks
Show steps
-
Sketch out the different layers and components of a CNN
-
Use online tools to create a digital representation
-
Share your representation with others for feedback
Discuss CNN applications in medical imaging
Show steps
Broaden your perspective on the use of CNNs in medical imaging by engaging in discussions with peers.
Browse courses on
Convolutional Neural Networks
Show steps
-
Join online forums or discussion groups
-
Participate in webinars or conferences on medical imaging
-
Reach out to researchers in the field
Participate in a Kaggle competition on skin cancer detection
Show steps
Challenge yourself by participating in a Kaggle competition to apply your skills in a real-world setting.
Show steps
-
Register for the Kaggle competition
-
Explore the competition dataset and familiarize yourself with the task
-
Develop and train a CNN model for skin cancer detection
-
Evaluate your model and submit your results
Contribute to an open-source CNN library
Show steps
Enhance your understanding of CNNs and contribute to the community by participating in an open-source project.
Browse courses on
Convolutional Neural Networks
Show steps
-
Identify an open-source CNN library to contribute to
-
Review the project's documentation and contribute to discussions
-
Identify areas where you can contribute code or documentation
-
Submit your contributions to the project
Review neural network fundamentals
Show steps
Reinforce your understanding of the fundamental concepts of neural networks, such as forward and backward propagation, to better grasp the course content.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Revisit online tutorials on neural network basics
- Review textbook chapters on neural network architectures
- Solve practice problems on neural network training
Follow tutorials on convolutional neural networks (CNNs)
Show steps
Delve deeper into the specific architecture of CNNs to better comprehend their application in the course.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Explore online tutorials on CNN architecture
- Complete hands-on exercises on CNN implementation
- Experiment with different CNN hyperparameters
Practice identifying benign and cancerous skin lesions
Show steps
Sharpen your ability to visually distinguish between benign and cancerous skin lesions, a key skill for the course.
Show steps
Show steps
Show steps
- Examine online databases of skin lesion images
- Participate in online forums for dermatology enthusiasts
- Consult with a dermatologist to gain expert insights
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a visual representation of the CNN architecture
Show steps
Enhance your understanding of CNN architecture by creating a visual representation, such as a diagram or infographic.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Sketch out the different layers and components of a CNN
- Use online tools to create a digital representation
- Share your representation with others for feedback
Discuss CNN applications in medical imaging
Show steps
Broaden your perspective on the use of CNNs in medical imaging by engaging in discussions with peers.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Join online forums or discussion groups
- Participate in webinars or conferences on medical imaging
- Reach out to researchers in the field
Participate in a Kaggle competition on skin cancer detection
Show steps
Challenge yourself by participating in a Kaggle competition to apply your skills in a real-world setting.
Show steps
Show steps
Show steps
- Register for the Kaggle competition
- Explore the competition dataset and familiarize yourself with the task
- Develop and train a CNN model for skin cancer detection
- Evaluate your model and submit your results
Contribute to an open-source CNN library
Show steps
Enhance your understanding of CNNs and contribute to the community by participating in an open-source project.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Identify an open-source CNN library to contribute to
- Review the project's documentation and contribute to discussions
- Identify areas where you can contribute code or documentation
- Submit your contributions to the project
Career center
Learners who complete Skin Cancer Detection will develop knowledge and skills
that may be useful to these careers:
Dermatologist
Dermatologist
Dermatologists focus on conditions of the skin and other areas of the body. These conditions could be either benign or cancerous. Dermatologists can use their expertise in conditions of the skin to help distinguish between the two. This course helps build a foundation in how to distinguish between the two skin tissue types.
Medical Doctor
Medical Doctor
Medical doctors, while a broad field, can specialize in dealing with conditions of the skin. Understanding how to detect skin cancer is in important part of this field. This course goes over important information which can be used to detect this condition.
Pathologist
Pathologist
Pathologists examine tissues to determine if they're cancerous or not. Cancer can appear in many different forms, including on the skin. As such, it is important to be able to tell the difference between skin tissue types. This course provides an overview of methods which can be used for this purpose.
Research Scientist
Research Scientist
Research scientists in the medical field can be involved in developing cures or treatments for cancer. This course provides a foundation in a specific area of cancer, which could be of interest to a research scientist in the medical field.
Health Educator
Health Educator
Health educators may provide support to those who are at risk for skin cancer, as well as those who have received a diagnosis. Knowledge of what skin cancer is and how to detect it is important in this role. This course can help health educators stay up to date on this information.
Nurse
Nurse
Nurses provide care to patients in a variety of healthcare settings. Nurses may help dermatologists or other doctors to care for patients with skin cancer.
Medical Assistant
Medical Assistant
Medical assistants help diagnose and treat patients in a variety of healthcare settings. Medical assistants may help dermatologists or other doctors to care for patients with skin cancer.
Data Scientist
Data Scientist
Data scientists analyze data and use it to form conclusions. This course may be useful for data scientists who are interested in working with medical data.
Software Engineer
Software Engineer
Software engineers who are interested in specializing in medical device development may find this course useful.
Biostatistician
Biostatistician
Biostatisticians work with medical data to help researchers make informed conclusions. This course provides an introduction to medical data and how to process it.
Health Policy Analyst
Health Policy Analyst
Health policy analysts develop and evaluate policies that affect the health of the public. This course may be useful for health policy analysts who are interested in working on skin cancer prevention.
Medical Writer
Medical Writer
Medical writers create written content about medical topics. This course may be useful for medical writers who are interested in writing about skin cancer.
Public Health Educator
Public Health Educator
Public health educators develop and deliver educational programs to promote health and prevent disease. This course may be useful for public health educators who are interested in teaching about skin cancer prevention.
Pharmacist
Pharmacist
Pharmacists dispense medications and provide information about their use. This course may be useful for pharmacists who are interested in learning more about skin cancer medications.
Epidemiologist
Epidemiologist
Epidemiologists study the causes of disease and how to prevent it. This course may be useful for epidemiologists who are interested in studying skin cancer.
For more career information including salaries, visit:
OpenCourser.com/course/1w9koj/skin
Reading list
We've selected five 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
Skin Cancer Detection.
Save
Comprehensive guide to deep learning for image recognition. It covers many of the same topics as the course. However, this book provides more background information and a more detailed treatment of the technical aspects of deep learning.
Comprehensive guide to machine learning and deep learning using Python. It covers many of the same topics as the course, but it provides more detailed explanations and a broader range of examples.
Concise and practical guide to deep learning with Python. It covers many of the same topics as the course, but provides more detailed explanations and a broader range of examples.
Save
Provides a comprehensive overview of artificial intelligence in medicine. It includes a chapter on the use of deep learning for skin cancer detection.
Provides a future look at healthcare and the role of artificial intelligence. It includes a chapter on the use of deep learning for skin cancer detection.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1w9koj/skin
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Time
120 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Cezanne Camacho
Jay Alammar
Alexis Cook
Luis Serrano
Juan Delgado
Ortal Arel
Language
English
Good to know
Taught by Sebastian Thrun who is noteworthy for their work in detecting skin cancer with neural networks
Provides a foundation for students interested in learning about convolutional neural networks and their applications in the medical field
Utilized by professionals in healthcare field, like dermatologists, to improve the early detection of skin cancer through the use of neural networks
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