I truly believe that artificial intelligence (AI) will shape our future and will bring tremendous impact and applications in industries such as health and agriculture. One of the things that I aim to achieve with dair.ai is to discuss interesting open-source AI technologies that help to address important problems such as medical diagnosis and personalized learning. One of the tools that have caught my attention this week is MedicalTorch (developed by Christian S. Perone), which is an open-source medical imaging analysis tool built on top of PyTorch. It contains a set of loaders, pre-processors and utility functions to efficiently and easily analyze medical images such as those acquired from magnetic resonance imaging (MRI) scans.
In this post, I will summarize some of the functionalities offered by the medicaltorch library and how it can be used to conduct medical imaging analysis. Specifically, this will be a tutorial on how to perform spinal cord gray matter segmentation using a technique based on convolutional neural networks (CNNs).