Deep Learning with PyTorch for Medical Image Analysis
What you'll learn
- Learn how to use NumPy
- Learn classic machine learning theory principals
- Foundations of Medical Imaging
- Data Formats in Medical Imaging
- Creating Artificial Neural Networks with PyTorch
- Use PyTorch-Lightning for state of the art training
- Visualize the decision of a CNN
- 2D & 3D data handling
- Automatic Cancer Segmentation
Requirements
- Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended
- Ideally PyTorch, but not necessarily required
Description
Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?
Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?
Then this is the right course for you!
Welcome to one of the most comprehensive courses on Deep Learning in medical imaging!
This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.
You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.
The following topics are covered:
NumPy
Machine Learning Theory
Test/Train/Validation Data Splits
Model Evaluation - Regression and Classification Tasks
Tensors with PyTorch
Convolutional Neural Networks
Medical Imaging
Interpretability of a network's decision - Why does the network do what it does?
A state of the art high level pytorch library: pytorch-lightning
Tumor Segmentation
Three-dimensional data
and many more
Why choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?
This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D)
All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step.
Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.
You will learn skills and techniques that the vast majority of AI engineers do not have!
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Jose, Marcel, Sergios & Tobias
Who this course is for:
- Python developers and Machine Learning engineers who want to learn how to tackle real world problems occurring on a daily basis in the field of medical imaging with the help of Deep Convolutional Neural Networks.
- Everybody who wants to learn more about the joint field of AI and Medical Imaging & how it works
- Developers familiar with basic Deep Learning knowledge who want to apply their skills to more than toy problems
- Medical professionals interested in how AI actually works in medicine
Instructors
- 4.6 Instructor Rating
- 1,266,257 Reviews
- 4,246,185 Students
- 87 Courses
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, SalesForce, Starbucks, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings.
- 4.6 Instructor Rating
- 1,880 Reviews
- 4,076,913 Students
- 2 Courses
Marcel Frueh has a Dr.rer.nat in Computer Science from the University of Tübingen.
His main goal is to connect the exciting field of machine learning with medicine to improve patients' lives.
He has years of experience working with medical imaging data, programming, Deep Learning, and as a university lecturer where he has taught hundreds of undergraduate and graduate students in various fields.
His main focus is to make complicated concepts simple to present and internalize through lots of practice.
Feel free to contact him on LinedIn for more information.
- 4.4 Instructor Rating
- 1,265 Reviews
- 10,794 Students
- 1 Course
I am a medical expert and machine learning scientist working on automated analysis of health data with a focus on automated medical image analysis. One of my goals is to disseminate knowledge on machine learning-based medical data analysis and to support beginners in successfully starting and completing their own medical machine learning projects.
- 4.4 Instructor Rating
- 1,265 Reviews
- 10,794 Students
- 1 Course
Tobias Hepp studied medicine and mathematics at the University of Tübingen and Stuttgart. He is currently working at the Max Planck Institute for Intelligent Systems in Tübingen as a Machine Learning Scientist. His research focuses on probabilistic methods, causality and deep learning for medical imaging in clinical and epidemiological applications
- 4.6 Instructor Rating
- 1,274,427 Reviews
- 4,178,650 Students
- 94 Courses
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