Automate the diagnosis of Knee Injuries with Deep Learning part 3: Interpret models' predictions

Posted on Mer 21 août 2019 in Computer vision, Deep Learning, Interpretability • Tagged with MRI, Medical Imaging, MRNet, CNN, PyTorch, interpretability, Class Activation Map, CAMLeave a comment


In this post, we will focus on interpretability to assess what the ACL tear detector we trained in the previous article actually learnt.
To do this, we'll explore a popular interpretability technique called Class Activation Map, applied when using convolutional neural networks that have a special architecture. By using this method, we'll highlight discriminative areas the network focus on before making a prediction when confronted with an image thus explaining the decision process and building trust.
CAM is also a generic method that can be applied to a variety of computer vision projects. So if you're looking for a way to make your CNNs interpretable you should read this tutorial and adapt the source code.


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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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