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3D Medical Image Analysis with PyTorch

  • Course Code: Data Science - 3D Medical Image Analysis with PyTorch
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 1 Days Audience: This course is geared for those who wants to use the deep learning framework PyTorch to implement a convolutional neural network for this task, and you will train it on the given paired data.

Course Snapshot 

  • Duration: 1 days 
  • Skill-level: Foundation-level 3D Medical Image Analysis with PyTorch skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to use the deep learning framework PyTorch to implement a convolutional neural network for this task, and you will train it on the given paired data. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our 3D Medical Image Analysis with PyTorch expert instructor, students 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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Training and Validation Data Setup 
  • Datasets and Transforms 
  • Create Your Neural Network 
  • Train the Network 
  • Evaluate the Results 

Audience & Pre-Requisites 

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. 

Pre-Requisites:  Students should have familiar with: 

TOOLS 

  • Basics of Matplotlib 
  • Basics of Jupyter Notebook 
  • Basics of Git 
  • Intermediate PyTorch 

TECHNIQUES 

  • Basics of gradient descent and SGD 
  • Basics of Loss functions 
  • Basics of Back-propagation 
  • Basics of neural networks 
  • Basics of advanced functions for ANNs such as softmax, sigmoid, ReLu 

Course Agenda / Topics 

  1. Training and Validation Data Setup 
  • Training and Validation Data Setup 
  • Volumetric Data 
  • Submit Your Work 
  1. Datasets and Transforms 
  • Datasets and Transforms 
  • Submit Your Work 
  1. Create Your Neural Network 
  • Create Your Neural Network 
  • Using Convolutions to Generalize 
  • Submit Your Work 
  1. Train the Network 
  • Train the Network 
  • The Mechanics of Learning 
  • Submit Your Work 
  1. Evaluate the Results 
  • Evaluate the Results 
  • Structuring Deep Learning Projects and Hyperparameters tuning 
  • Submit Your Work 
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