The XNAT ML (Beta) release is a stand-alone package that combines a pre-release version of XNAT 1.8 with several platform and plugin additions to enable a baseline of Machine Learning capability. These capabilities were first demonstrated in collaboration with the NVIDIA Clara suite of medical imaging AI tools, at RSNA 2019. Since then, we have been expanding functionality, deepening integrations, and hardening features to get to the point of this release.

Getting Started

Download XNAT ML (Beta)

• Components of the XNAT ML (Beta) Release

Docker Compose Installation Instructions

• Docker Server Setup
• Docker Compose Installation Instructions
• Rebuilding Your Docker Compose Environment
• DevOps Quick Reference for Containerized XNATs

Project Configuration

• Creating a Project and Assigning Roles
  
• Using the Machine Learning Project Dashboard

Container Service Configuration

• Configure the Container Service Plugin
• Install Docker Images and Commands
• Configure Installed Commands

DQR / PACS Configuration

• Connecting XNAT to your Demo Orthanc PACS

Performing Training

Workflows

• Overview of supported workflows
  • Model Training: Pre-coordinated Workflow
  • Model Training: Retrospective Workflow
  • Inference (currently unsupported)

Setup Tasks

• Installing a Model
• Creating a Training Config for your Model
• Configuring a Segmentation Mapping Resource for your Model
• Set up an Automated Workflow to Generate Required Model Resources

Data Import and Collection

• Importing DICOM from a PACS via the DQR plugin
• Drawing Contours with the XNAT OHIF Viewer
• Data Conversion for Model Training
• Defining Parameters for your Dataset
• Validating and Saving your Dataset from Project Data

Model Training

• Launching Initial Training
• Launching Finetune Training
• Viewing Training Progress in TensorBoard
• Viewing Training Logs
• Accessing and Downloading Training Results
• Promoting Training Results as a new "Model"

Other Operations

• Deleting Models, Training, and Datasets