Deep Learning with PyTorch for Medical Image Analysis

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

Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?

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.

Course Content

  • Introduction –> 5 lectures • 26min.
  • Crash Course: NumPy –> 7 lectures • 49min.
  • Machine Learning Concepts Overview –> 5 lectures • 42min.
  • PyTorch Basics –> 7 lectures • 55min.
  • CNN – Convolutional Neural Networks –> 16 lectures • 2hr 50min.
  • Medical Imaging – A short Introduction –> 5 lectures • 19min.
  • Data Formats in Medical Imaging –> 8 lectures • 1hr 15min.
  • Pneumonia-Classification –> 7 lectures • 1hr 25min.
  • Cardiac-Detection –> 7 lectures • 1hr 3min.
  • Atrium-Segmentation –> 9 lectures • 1hr 18min.
  • Capstone-Project: Lung Tumor Segmentation –> 4 lectures • 18min.
  • 3D Liver and Liver Tumor Segmentation –> 6 lectures • 42min.
  • BONUS SECTION: THANK YOU! –> 1 lecture • 1min.

Deep Learning with PyTorch for Medical Image Analysis

Requirements

  • Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended.
  • Ideally PyTorch, but not necessarily required.

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 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