Training an object classifier in QuPath involves creating annotations, assigning classes, and using the “Load training” feature to build a reliable model for image analysis. Here’s a step-by-step guide:

1. Create Annotations

• Use QuPath’s annotation tools to mark regions of interest (ROIs) in the images.
• These annotated regions will serve as the training data for your classifier.

2. Assign Classes

• After annotating, assign each annotation to a specific class (e.g., tumor, stroma).
• Do this by selecting the annotation and setting its class in the Annotations tab.

3. Train the Classifier

• Go to Classify > Object Classification > Train Object Classifier.
• In the training dialog, click “Load Training” to select images with existing annotations for training.
• This step allows you to incorporate annotations from multiple images to improve classifier performance.
• (Optional) Enable “Live Update” to visualize real-time classification results as you adjust settings.

4. Select Features

• Click “Edit” in the classifier window.
• Choose the features the classifier should consider (e.g., intensity, texture, shape).
• Selecting relevant features can significantly improve classification accuracy.

5. Save the Classifier

• Once satisfied with the classifier’s performance, save it via:
  Classify > Object Classification > Save Object Classifier.
• The saved classifier can be reused for consistent analysis across multiple projects.

Key Tip: Using the “Load Training” Feature

The “Load Training” feature is especially useful when working with multiple images in a project. It allows the classifier to:

• Incorporate annotations from multiple images.
• Improve robustness and accuracy by learning from diverse samples.

Conclusion

By following these steps, you can create a reliable object classifier in QuPath for consistent image analysis. For a visual demonstration, consider exploring official QuPath video tutorials or user guides.