# Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation

Source: [Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation](<https://www.sota2.com/research/paper/68fa760f1e209131cea6b30c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:11.697Z

Authors: Il\\'an Carretero, Pablo Meseguer, Roc\\'io del Amor, Valery Naranjo

Published: 2024-12-05T15:39:54.000Z

## Abstract

Domain shift in the field of histopathological imaging is a common phenomenon due to the intra- and inter-hospital variability of staining and digitization protocols. The implementation of robust models, capable of creating generalized domains, represents a need to be solved. In this work, a new domain adaptation method to deal with the variability between histopathological images from multiple centers is presented. In particular, our method adds a training constraint to the supervised contrastive learning approach to achieve domain adaptation and improve inter-class separability. Experiments performed on domain adaptation and classification of whole-slide images of six skin cancer subtypes from two centers demonstrate the method's usefulness. The results reflect superior performance compared to not using domain adaptation after feature extraction or staining normalization.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.04260>)

[PDF](<https://arxiv.org/pdf/2412.04260.pdf>)