Learn how build an end to end machine learning application from scratch. To do this, we'll walk you through the process of collecting data, training a deep learning model, building a Dash application, putting everything in Docker and deploying to AWS. This post is a little bit longer than usual but the different parts are independant and reusable in other projects
In this last 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 (CAM), applied when using convolutional neural networks that have a special architecture
In this post, you'll learn how to use PyTorch to train an Anterior Cruciate Ligament (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
This is a first post of a series dedicated to medical imaging using deep learning. Today, we'll explore an MRI dataset of knee injuries