3D Medical Image Analysis with PyTorch

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prerequisites
intermediate Python • intermediate PyTorch • basics of deep learning (CNNs)
skills learned
train a neural network for a regression task • build a CNN • handle and visualize medical imaging data
Jacob Reinhold
5 weeks · 8-10 hours per week · ADVANCED
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Look inside
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 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

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choose your plan

pro

monthly
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$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
  • 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
  • 3D Medical Image Analysis with PyTorch project for free