A novel H and E color augmentation for domain invariance classification of unannotated histopathology prostate cancer images

Abstract

Most current deep learning models for hematoxylin and eosin (H&E) histopathology image analysis lack the power of generalization to datasets collected from other institutes due to the domain shift in the data. In this research, we study the domain shift problem on two prostate cancer (PCa) datasets collected from the Vancouver Prostate Centre (source dataset) and the University of Colorado (target dataset) and develop a novel centerbased H&E color augmentation for cross-center model generalization. While previous work used methods such as random augmentation, color normalization, or learning domain-independent features to improve the robustness of the model to changes in H&E stains, our method first augments the H&E color space of the source dataset to color space of both datasets and then adds random color augmentation. Our method covers the larger range of the color distribution of both institutions resulting in a better generalization. We compared our method with two different State-Of-The-Art (SOTA) un-annotated domain adaptation methods: color normalization and unsupervised domain adversarial neural network (DANN) training, with an ablation study. Our proposed method improves the model performance on both the source and target datasets, and has the best performance on the unlabeled target dataset, showing promise as an approach to learning more generalizable features for histopathology image analysis.

Prostate Cancer Classification Based on Histopathological Images

“…Their approach, validated on an independent dataset, effectively discriminates cancer grade from benign tissue, achieving remarkable accuracies of 91% and an AUC of 0.96. Bazargani et al [32] addressed the domain shift challenge in histopathology image analysis by introducing a novel centre-based H&E colour augmentation technique. This approach enhances the generalization power of deep learning models across datasets from different institutes, demonstrating promise in learning more generalizable features for histopathology image analysis.…”