Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.

Area of Science:

• Computational Pathology
• Digital Pathology
• Machine Learning in Medicine

Background:

• Deep learning (DL) in computational pathology (CPath) struggles with generalizing to new data due to high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), including hidden stratification and batch effects.
• Domain Adaptation (DA) is crucial for applying DL models trained on source data to target domains with limited or no annotations, a common scenario in CPath.

Purpose of the Study:

• To evaluate the effectiveness of different domain adaptation strategies for generalizing DL models in CPath from a single source to a target domain.
• To assess the suitability of FixMatch, CycleGAN, and self-supervised feature extraction for addressing DA challenges in H&E-stained WSIs.

Main Methods:

• Investigated three distinct domain adaptation techniques: FixMatch (a semi-supervised learning approach), CycleGAN (a generative adversarial network for image-to-image translation), and a self-supervised feature extractor.
• Focused on the specific task of generalizing from a single source data distribution to a target data distribution within the CPath domain.

Main Results:

• The study demonstrated that generalizing deep learning models beyond the training distribution in CPath remains a significant challenge, even with established DA strategies.
• Performance metrics indicated that none of the evaluated DA methods consistently overcame the inherent difficulties posed by WSI variability and domain shift.

Conclusions:

• Current domain adaptation strategies, including FixMatch, CycleGAN, and self-supervised learning, are not yet fully optimized for the complexities of computational pathology.
• Further research is needed to develop more robust DA techniques tailored to the unique characteristics of H&E-stained WSIs to improve model generalization in CPath.