Automate the diagnosis of Knee Injuries with Deep Learning part 2: Building an ACL tear classifier

Posted on Dim 14 juillet 2019 in Computer vision, Deep Learning • Tagged with MRI, Medical Imaging, MRNet, CNN, PyTorch, image classificationLeave a comment


In this post, you'll build up on the intuitions you gathered on MRNet data by following the previous post. You'll learn how to use PyTorch to train an ACL tear classifier that sucessfully detects these injuries from MRIs with a very high performance. We'll dive into the code and we'll go through various tips and tricks ranging from transfer learning to data augmentation, stacking and handling medical images. You'll also learn about optimization tricks as well as how to organize your code efficiently. If you're a crafty AI engineer who wants to play with code to learn how things work, just keep reading !


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Automate the diagnosis of Knee Injuries with Deep Learning part 1: an overview of the MRNet Dataset

Posted on Mar 25 juin 2019 in Computer vision, Deep Learning • Tagged with MRI, MLedical Imaging, MRNet, CNN, PyTorch, image classificationLeave a comment


If you are interested in learning an impactful medical application of artificial intelligence, this series of articles is the one you should looking at.
My goal is to show you how you can use deep learning and computer vision to assist radiologists in automatically diagnosing severe knee injuries from MRI scans.
To do this, we'll first explore the MRNet dataset in this first post. We'll then build a deep learning classification model in PyTorch in the next post and develop an interpretation pipeline in the last one.
By the end, you'll have an overview of a medical imaging application with different components that you can use elsewhere in similar situations.
Let's start.


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