6 Tips to Help Your Model Generalize to Different Labs and Scanners
Does your machine learning model perform well on your training data but fail miserably when presented with images from a different source?
- Is a domain shift killing your model performance?
- Have you tried a variety of techniques to improve generalizability to no avail?
- Is model generalizability critical to the success of your product or service?
- Do you need your project unstuck?
Imagine having confidence in the robustness of your models
- Imagine that your models perform well not just on your training dataset but also on external cohorts
- Imagine having a toolbox to draw from when you encounter domain shifts
- Imagine knowing which techniques to try for your situation
- Imagine your project progressing smoothly
My FREE tips to improving model generalizability are the answer
This guide will outline 6 techniques that you can use to boost your model performance. These approaches are validated by the latest research and have been put to use by many of the leaders in the industry.
What you’ll learn:
- Why handling domain shifts is critical
- 6 techniques for improving model generalizability to different labs and scanners
- When to use each approach
- When a particular technique may not be appropriate