H&E Stain Normalization

Problem: One of the largest challenges in histopathology image analysis is creating models that can handle the variations across different labs and imaging systems. These variations can be caused by different color responses of slide scanners, raw materials, manufacturing techniques, and protocols for staining. Different setups can produce images with different stain intensities or other changes, creating a domain shift between the source data that the model was trained on and the target data on which a deployed solution would need to operate. When the domain shift is too large, a model trained on one type of data will fail on another, often in unpredictable ways. The goal of stain normalization is to standardize the appearance of these stains.

Solution: The goal of this project was to evaluate a variety of stain normalization methods and their effectiveness in reducing domain shift for downstream analysis tasks. We ran both traditional and deep learning-based methods on three different H&E datasets that include whole slide images from different scanners. We assessed each with respect to accuracy on a downstream task, ease of training, computation requirements, and failure modes.