PyTorch
PyTorch Introduction PyTorch is a machine learning framework based on the Torch library, used for applications such as Computer Vision and Natural Language Processing, originally developed by Meta AI and now part of the Linux Foundation umbrella.
A lot of AI frameworks can be used within MeVisLab. We currently do not provide a preintegrated AI framework though as we try to avoid compatibility issues, and AI frameworks are very fast-moving by nature.
Example 1: Installing PyTorch using the PythonPip module Introduction The module PythonPip allows you to install additional Python packages to be used in MeVisLab.
Warning: You should not use the general Python pip command from a locally installed Python, because MeVisLab will not know these packages and they cannot be used in MeVisLab directly. The module either allows to install packages into the global MeVisLab installation directory, or into your defined user package.
Example 2: Brain Parcellation using PyTorch Introduction In this example, you are using a pre-trained PyTorch deep learning model (HighRes3DNet) to perform a full brain parcellation. HighRes3DNet is a 3D residual network presented by Li et al. in On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task.
Steps to do Add a LocalImage module to your workspace and select the file MRI_Head.dcm. For PyTorch it is necessary to resample the data to a defined size.
Example 3: Segment persons in webcam videos Introduction This tutorial is based on Example 2: Face Detection with OpenCV. You can re-use some of the scripts already developed in the other tutorial.
Steps to do Add the macro module developed in the previous example to your workspace.
WebCamTest module Open the internal network of the module via middle mouse button and right click on the tab of the workspace showing the internal network.
