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

## TL;DR

This paper compares different domain adaptation strategies to improve generalization in computational pathology using H&E-stained images.

## Contribution

The study evaluates FixMatch, CycleGAN, and self-supervised learning for domain adaptation in computational pathology.

## Key findings

- Domain adaptation remains a significant challenge in computational pathology.
- FixMatch, CycleGAN, and self-supervised methods show varying effectiveness in adapting to new domains.
- Results highlight the need for further research into robust domain adaptation strategies for histopathology images.

## Abstract

Due to the high variability in Hematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs), hidden stratification, and batch effects, generalizing beyond the training distribution is one of the main challenges in Deep Learning (DL) for Computational Pathology (CPath). But although DL depends on large volumes of diverse and annotated data, it is common to have a significant number of annotated samples from one or multiple source distributions, and another partially annotated or unlabeled dataset representing a target distribution for which we want to generalize, the so-called Domain Adaptation (DA). In this work, we focus on the task of generalizing from a single source distribution to a target domain. As it is still not clear which domain adaptation strategy is best suited for CPath, we evaluate three different DA strategies, namely FixMatch, CycleGAN, and a self-supervised feature extractor, and show that DA is still a challenge in CPath.