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Updated: Aug 31, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.

Xiaofeng Liu¹, Chaehwa Yoo^1,2, Fangxu Xing¹

¹Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Proceedings of Spie--The International Society for Optical Engineering

|August 19, 2022

Summary

This study introduces a novel unsupervised domain adaptation (UDA) method for segmentation using only a black-box model, addressing data privacy concerns. The approach achieves performance comparable to traditional methods without needing source data or white-box models.

Keywords:

Black-box source model Brain MR image segmentation Unsupervised domain adaptation

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Area of Science:

Computer Vision
Machine Learning
Medical Image Analysis

Background:

Unsupervised Domain Adaptation (UDA) is crucial for transferring knowledge to new domains with limited labeled data.
Privacy concerns and data access limitations hinder cross-center collaborations in traditional UDA.
Existing UDA methods often require access to source domain data or a white-box source model.

Purpose of the Study:

To propose a practical UDA solution for segmentation that overcomes data privacy and model access limitations.
To enable knowledge transfer from a source domain using only a black-box segmentation model.
To develop a privacy-preserving UDA framework for cross-domain collaborations.

Main Methods:

A knowledge distillation scheme with exponential mixup decay (EMD) was employed to learn target-specific representations.
Unsupervised entropy minimization was used for regularization of target domain confidence.
The framework operates on a black-box source model, eliminating the need for source data or white-box access.

Main Results:

The proposed framework achieved performance on par with white-box source model adaptation approaches.
Effective knowledge transfer was demonstrated from the source to the target domain.
The method successfully addressed privacy concerns associated with source data and models.

Conclusions:

The developed UDA framework offers a practical and privacy-preserving solution for segmentation tasks.
This approach facilitates cross-center and cross-domain collaborations by removing data sharing barriers.
The method shows significant potential for real-world applications where data privacy is paramount.