👉 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 ↓
At PYCAD, we constantly work on deploying medical imaging applications on the cloud.
Here are 2 ways that we’ve used to do deployment:
1 - Google Cloud Run:
This is a service from Google Cloud Platform (GCP) where you can easily deploy containerized applications with few steps and without managing much of the infrastructure.
Pros:
Cons:
2 - Google Compute Engine:
This is another service from GCP which allows you to rent a virtual machine (VM) with the configuration that you like. You can choose the number of CPUs, GPUs and how much RAM you want.
Pros:
Cons:
As you can see, both approaches are on GCP. This is because we really like this cloud provider. Though they have less services than AWS, but they have very thorough documentation and many tutorials.
I should also mention that these are not the only 2 ways to deploy web apps on GCP. Also, we didn’t delve deep into the best way to deploy a medical imaging app if it contains deep learning models inside. Maybe I'll do this in the future.
My Brother Mohammed has released a free open source library for medical imaging tasks. It's a library that allows you to do many things such as:
Check out the library github repo.
Show him some love by adding a star (⭐) to that repo!
Moreover, you can easily figure out how to use the library through a chatbot interface where you can ask questions about what the library can do and also how to do specific things with it, such as converting formats or visualization.
You can test the chatbot live here.
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We Can Help You with Your Next Medical Imaging ProjectIf 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: 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. |
That's it for this week's edition, I hope you enjoyed it!
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!
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 ↓ 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 ↓ 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...