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Machine Learning for Medical Imaging

10 tools we use to build medical imaging solutions

Published about 2 months ago • 2 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 ↓

10 Tools We Use to Build Medical Imaging Products

Since we work fully on medical imaging problems at our development agency, we have experimented with and used lots of libraries. Here are 10 libraries we use again and again:

PyDicom

This library helps us when dealing with DICOM data , which is a recurring thing for us.

SimpleITK

This library helps us do all sorts of image processing on medical data including I/O operations and applying filters.

Pytorch

This library helps us build ML models to solve medical imaging problems like radiograph classification or anatomical structures segmentation.

Docker

This helps us build containerized applications which makes deployment of our solutions an ease.

AWS SageMaker

We use it to deploy scalable ML models for medical imaging.

OpenCV

This library helps us with lots of 2D imaging parts. It's an industry standard in computer vision, so it makes sense we use it for medical imaging. Especially with 2D radiographs.

Streamlit

A lot of our clients want to build minimum viable products quickly. With this library we can do exactly that. It helps us spin up a user interface rather quickly, all while using Python.

FastAPI

We use it to build RESTful APIs for deep learning models.

VTK

We use this library to do all sorts of operations on 3D meshes. This is very useful when you reconstruct a 3D model of an anatomical structure.

NIBABEL

We use this library to deal with NIFTI files. A lot of our clients use NIFTI format to store medical imaging information. So this library allows us to read NIFTI files and get information about pixel data as well as metadata.

Foundation ML Model for Medical Image Registration

There is a new machine learning model that serves as a foundation model for medical image registration. The model is called uniGradICON. It unifies the speed and accuracy benefits of learning-based registration algorithms with the generic applicability of conventional non-deep-learning approaches.

You can find out more about it in its original paper as well as its github rep.

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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.


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!

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