# Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization

Source: [Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization](<https://www.sota2.com/research/paper/68fa5ef31e209131ce9e67b4>)

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

Data updated: 2025-10-25T19:04:52.118Z

Authors: Yu Zhang, Gongbo Liang, Nathan Jacobs, Xiaoqin Wang

Published: 2020-03-02T02:42:43.000Z

## Abstract

Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.

## 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/2003.01111>)

[PDF](<https://arxiv.org/pdf/2003.01111.pdf>)