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Course Skill Level:


Course Duration:

1 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Experienced Python programmers with basic experience in Matplotlib, Jupyter Notebook, Git, PyTorch, Gradient Descent & SGD, loss functions, back-propagation, neural networks, and advanced functions for ANNs

Who should attend & recommended skills

  • This course is geared for those who want to use the deep learning framework PyTorch to implement a convolutional neural network for this task. You will train on the given paired data.
  • This course is for experienced Python programmers familiar with object-oriented programming techniques and Python scientific computing packages. You will need to know the basics of machine learning and statistics, but this course will teach you the advanced techniques. Throughout, you’ll use the Google Collaboratory (Colab) coding environment to access free GPU computer resources and speed up your training times.
  • Skill-level: Foundation-level 3D Medical Image Analysis with PyTorch skills for Intermediate skilled team members. This is not a basic class.
  • Matplotlib: Basic (1-2 years’ experience)
  • Jupyter Notebook: Basic (1-2 years’ experience)
  • Git: Basic (1-2 years’ experience)
  • PyTorch: Intermediate (3-5 years’ experience)
  • Gradient Descent and SGD: Basic (1-2 years’ experience)
  • Loss Functions: Basic (1-2 years’ experience)
  • Back-propagation: Basic (1-2 years’ experience)
  • Neural Networks: Basic (1-2 years’ experience)
  • Advanced Functions for ANNs such as softmax, sigmoid, ReLu: Basic (1-2 years’ experience)

About this course

In this course, you will be filling the role of a machine learning engineer/researcher at a healthcare technology company specializing in medical imaging applications. Your team wants to process and analyze magnetic resonance (MR) images of the brain. An MR imaging system is a flexible device that can create multiple types of images based on what a physician wants to see, but not all types of images are acquired in every scan due to time constraints. Your current processing and analysis algorithms require two types of MR images, but a new set of customer data only has one of those types. However, you have access to a fairly large, preprocessed dataset of paired examples of the two types of MR images, and you decide that deep learning would best perform this type of image transformation task.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our 3D Medical Image Analysis with PyTorch expert instructor, participants will learn about and explore:
  • How to load and process imaging data for deep learning applications
  • How to build a convolutional neural network
  • How to train a neural network for a regression task
  • How to evaluate the predictions of your neural network
  • How to handle and visualize medical imaging data
  • Training and Validation Data Setup
  • Datasets and Transforms
  • Create Your Neural Network
  • Train the Network
  • Evaluate the Results

Course breakdown / modules

  • Training and Validation Data Setup
  • Volumetric Data
  • Submit Your Work

  • Datasets and Transforms
  • Submit Your Work

  • Create Your Neural Network
  • Using Convolutions to Generalize
  • Submit Your Work

  • Train the Network
  • The Mechanics of Learning
  • Submit Your Work

  • Evaluate the Results
  • Structuring Deep Learning Projects and Hyperparameters tuning
  • Submit Your Work