👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
You'll like what we just released!
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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 ↓ 3 Resources we released in the past 2 weeks for nnUNetIn the past 2 weeks we released a ton of information about how to nnUNet to build powerful medical imaging segmentation models. Here's a summary of them. X Post of the DayGoogle introduces Explainable AI in Medical Imaging!
TotalSegmentator Has a New Feature!If you've been a subscriber for a long time then you've probably seen me cover TotalSegmentator multiple times! This is because, the folks behind it keep updating it with new awesome features every few weeks or months. The latest feature? TotalSegmentator now supports MR images! It can segment 59 anatomical structures in MR images, regardless of sequence or contrast! You can find out more about it in the official github repo and also in the original paper. Btw, TotalSegmentator is based on nnUNet models! So if you plan to build AI models similar to what TotalSegmentator is doing, then the guides we shared above would be a great starting point for you!
That's it for this week's edition, I hope you enjoyed it! |
Machine Learning for Medical Imaging
by Nour Islam Mokhtari from pycad.co
👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
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 ↓ 3D Printing for Medical Imaging I attended an event about 3D printing for medical applications. Here are some surprising insights.1 - You can use 3D printers to print fully ready...
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 ↓ 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...
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 ↓ How 3D Medical Images Impact my ML Pipelines at several steps I used to mostly deal with 2D images. But for the past couple of years, I’ve been dealing with 3D medical images....