Improving the Generalizability of Histopathology Classification

Problem: Training models that are robust to staining and scanner variations is one of the largest challenges in computational pathology. The most common solutions include color augmentation, stain normalization, and domain adversarial training. Model generalizability can be further impeded by variations in the patient population.

Solution: This project involved digging deep to reveal as many tactics as possible for improving model generalizability across a large set of models for different cancers and biomarkers. Solutions included histopathology-specific variants of color augmentation and domain adversarial training, as well as pretraining approaches like self-supervised learning. By adapting methods to the unique characteristics of H&E whole slide images, we can build models that generalize better to the variations present in these images.