π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Transformers for Medical Imaging
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 β
Transformers for Medical Imaging
Transformers are being used everywhere, but did you know that you can use them with 3D medical data?
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Several transformers based architectures have been designed to work with 3D medical data. One of these architectures is called UNETR.
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UNETR stands for UNEt TRansformers.
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Below you can see a high level overview of the architecture.
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And guess what?
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You can find this architecture already implemented and ready to be trained using MONAI framework!
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You can learn more about UNETR in its original paper.
You can find implementation of the network as well as how to train it here.
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New Version of TotalSegmentator
The new version of TotalSegmentator is here and itβs impressive!
This model can segment 100+ anatomical structures!
New features mentioned by Jakob Wasserthal (creator and main contributor of TotalSegmentator):
- 33 new anatomical structures
- up to 32x faster runtime on a CPU.
- up to 5x faster runtime on a GPU.
- improved label quality.
- larger training dataset (n=1559).
- uses nnU-Net v2.
Some of the new classes:
- skull,
- thyroid gland,
- prostate,
- brachiocephalic vein left.
You can test the model yourself here.
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Tweet of the Day
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Meme of the Day π
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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:
βcontact@pycad.coβ
We can help you build a full ML solution from training to deployment in record time and with affordable rates!
You can check out some of the projects that we worked on here:
βhttps://pycad.co/portfolioβ
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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
π Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | π‘ The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hi Reader,, Welcome to the PYCAD newsletter, where every week you receive doses of machine learning and computer vision techniques and tools to help you learn how to build AI solutions to empower the most vulnerable members of our society, patients. TotalSegmentator : Whole Body Segmentation at your Fingertips This free tool available online can do full body segmentation, it's called TotalSegmentator. I have already mentioned this tool in a previous edition of the newsletter, but in this...
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 β A Medical Imaging Expert Told Me This Recently I saw a post on LinkedIn where a medical imaging expert showcased his work of segmenting the lungs and its bronchial trees. You can...
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 β How we helped accelerate inference time for a client's AI product Below is a screenshot of a benchmark we did for a client of ours. The goal was to accelerate inference time. This...