# Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification

Source: [Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification](<https://www.sota2.com/research/paper/68fa6ca21e209131cea3519b>)

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

Data updated: 2025-10-26T00:15:46.115Z

Authors: Wenshuo Zhou, Dalu Yang, Binghong Wu, Yehui Yang, Junde Wu, Xiaorong Wang, Lei Wang, Haifeng Huang, Yanwu Xu

Published: 2022-05-31T09:54:17.000Z

## Abstract

Deep learning based medical imaging classification models usually suffer from the domain shift problem, where the classification performance drops when training data and real-world data differ in imaging equipment manufacturer, image acquisition protocol, patient populations, etc. We propose Feature Centroid Contrast Learning (FCCL), which can improve target domain classification performance by extra supervision during training with contrastive loss between instance and class centroid. Compared with current unsupervised domain adaptation and domain generalization methods, FCCL performs better while only requires labeled image data from a single source domain and no target domain. We verify through extensive experiments that FCCL can achieve superior performance on at least three imaging modalities, i.e. fundus photographs, dermatoscopic images, and H & E tissue images.

## 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/2205.15658>)

[PDF](<https://arxiv.org/pdf/2205.15658.pdf>)