Working with Weka
Weka is is powerful data mining software from the Machine Learning Group at the University of Waikato.
While not explicitly aimed towards image analysis applications, the Weka software provides Java implementations of a wide range of machine learning classifiers.
The QuPath Weka extension provides a collection of methods to wrap up the features of QuPath's objects in appropriate Weka-friendly way, and provides access to several of Weka's most popular classifiers directly from within QuPath.
Additionally, the Weka extension makes it possible to export QuPath object features in Weka's default file format (arff) to load into Weka's suite of data exploration and visualization tools, including Weka Explorer and Experimenter. This enables more detailed interrogation of the data, including the ability to explore many more different classifiers and variations on their parameters, cross-validation, feature selection algorithms and clustering.
These docs are for QuPath ≤ v0.1.2.
For more up-to-date information, see https://qupath.readthedocs.io
- Video tutorials
- First steps
- Viewing images
- Drawing regions
- Counting cells
- Projects
- Multiple images
- Preferences
- Getting help
- Object-oriented analysis
- Types of object
- Object measurements
- Object classifications
- Object hierarchies
- Working with objects
- Workflows
- From workflows to scripts
- Writing custom scripts
- Advanced scripting with IntelliJ
- Scripting examples