Deep Learning with PyTorch for Medical Image Analysis

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

What You Will 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

Who Should Attend!

• 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

TAKE THIS COURSE