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Machine Learning for Medical Imaging

nnUNet guide for medical image segmentation

Published about 1 month agoΒ β€’Β 1 min read

Hello Reader,

Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓

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nnUNet for Medical Imaging Segmentation

We've been using nnUNet a lot in our projects and it's an incredible framework for building powerful medical imaging segmentation models.

I wrote a detailed guide on how to use it with your own data. Check it out here.

Btw, my brother will be releasing the video version of this guide soon. It will be a different application than the one covered in the written tutorial. This way you'll get 2 different tutorials on how to use nnUNet with your own custom data!

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We Can Help You with Your Next Medical Imaging Project

If your company or organization is looking to build a machine learning solution for a medical imaging problem, then feel free to reach out to us at:

​contact@pycad.co​

We can help you build a full ML solution from training to deployment with affordable rates!

You can check out some of the projects that we worked on here:

​https://pycad.co/portfolio and some of our clients case studies here.

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That's it for this week's edition, I hope you enjoyed it!

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Machine Learning for Medical Imaging

by Nour Islam Mokhtari from pycad.co

πŸ‘‰ Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | πŸ’‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!

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