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Learn how to use Pytorch-Lightning to solve real world medical imaging tasks!
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
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
Overview
