Are your computer vision models consistently delivering fair and accurate results across all patient groups and data sources? If you’re not actively managing bias and batch effects, the answer might surprise you.
These challenges can:
- Skew model predictions unfairly across demographics or acquisition conditions.
- Reduce generalizability when deploying models in real-world scenarios.
- Create spurious correlations that lead to misleading conclusions.
In this 30-minute webinar, I share:
- Key Concepts: What are bias, batch effects, and spurious correlations—and why do they matter?
- Real-World Examples: How subtle artifacts or data variations can derail model performance.
- Actionable Strategies: Practical techniques like data splitting by groups and stratified metrics to prevent bias and improve fairness. Plus – how do foundation models impact batch effects?
Whether you’re a data scientist, ML engineer, product manager, or business leader implementing computer vision solutions, this session will equip you with actionable techniques to build more reliable and equitable AI systems.
In this session, I explored how subtle biases can significantly impact AI models in medical imaging, potentially causing them to rely on shortcuts rather than clinically relevant features. Did you know deep learning can predict race from radiology images or medical center from histopathology slides? These capabilities raise important questions about what our models are actually learning.
Key takeaways:
- Bias can come from multiple sources: dataset representation, sampling methods, scanner variations, tissue preparation, and annotation subjectivity
- Batch effects are particularly prevalent in histopathology (tissue thickness, staining protocols, scanner variability)
- Detecting bias requires both domain expertise and rigorous validation techniques
- Mitigation strategies include careful cross-validation design, data harmonization, and model architecture innovations
- Even foundation models aren’t immune - most still encode site-specific features!