In this liveProject, you’ll take on the role of a machine learning engineer at a healthcare imaging company, processing and analyzing magnetic resonance (MR) brain images. Your current medical image analysis pipelines are set up to use two types of MR images, but a new set of customer data has only one of those types! Your challenge is to build a convolutional neural network that can perform an image translation to provide you with your missing data. You’ll do this using the deep learning framework PyTorch and a large preprocessed set of MR brain images. The company also wants to make sure your image translation convolutional neural network reliably produces the desired MR image, so you will need to provide qualitative and quantitative results demonstrating your method’s effectiveness.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
project
$49.99
$37.49
you save $12.50 (25%)
with subscription
$24.99
project
$224.99
project author
Jacob Reinhold
Jacob Reinhold is a PhD student in electrical engineering at Johns Hopkins University. His research focuses on medical image analysis, specifically in applying deep learning techniques and theory to study anomaly detection in magnetic resonance and computed tomography images. His work has been published in peer-reviewed journals and conferences in the field. Prior to his graduate studies, he worked as an engineering scientist associate in the Applied Research Laboratories at the University of Texas at Austin and received his BS in electrical engineering at the University of Texas at Austin.
prerequisites
This liveProject 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. To begin this liveProject, you will also need to be 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
features
Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from fellow participants and even more help with paid sessions with our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
related titles
related titles
choose your plan
pro
monthly
annual
$24.99
$249.99
only $20.83 per month
access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
choose another free product every time you renew
choose twelve free products per year
exclusive 50% discount on all purchases
renews monthly, pause or cancel renewal anytime
renews annually, pause or cancel renewal anytime
3D Medical Image Analysis with PyTorch project for free
team
monthly
annual
$49.99
$399.99
only $33.33 per month
five seats for your team
access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
choose another free product every time you renew
choose twelve free products per year
exclusive 50% discount on all purchases
renews monthly, pause or cancel renewal anytime
renews annually, pause or cancel renewal anytime
3D Medical Image Analysis with PyTorch project for free