Deep Learning with PyTorch for Medical Image Analysis

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.