# Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets

arXiv: https://arxiv.org/abs/2607.06549 · HTML: https://mlsanity.com/paper/2607.06549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data](https://mlsanity.com/paper/2004.04742/) (2020)
- [The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN](https://mlsanity.com/paper/1910.13681/) (2019)
- [Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach](https://mlsanity.com/paper/1912.11027/) (2019)
- [A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification](https://mlsanity.com/paper/1707.06978/) (2017)
- [Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms](https://mlsanity.com/paper/2006.00086/) (2020)
- [Domain Generalization for Mammographic Image Analysis with Contrastive Learning](https://mlsanity.com/paper/2304.10226/) (2023)
- [Large-scale mammography CAD with Deformable Conv-Nets](https://mlsanity.com/paper/1902.07323/) (2019)
- [SCREENet: A Multi-view Deep Convolutional Neural Network for Classification of High-resolution Synthetic Mammographic Screening Scans](https://mlsanity.com/paper/2009.08563/) (2020)

## Abstract (arXiv)

Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets remain a challenge, especially when models are applied to unseen domains. In this work, we proposed a calcification classification framework to improve malignant versus benign breast disease classification across multi-site mammography datasets. The framework consisted of two components: (1) an unsupervised domain adaptation module based on style transfer models (AdaIN and CycleGAN) to generate vendor-specific and technique-specific training samples without additional annotations, and (2) a supervised classification module using Swin Transformer V2 as the backbone. We evaluated the proposed method on three datasets: cross-validation on OPTIMAM (National Health Service, United Kingdom; n=2994), followed by external validation on EMBED (Emory University; n=125), and Duke Calcification Dataset v1 (n=788). These datasets cover multiple vendors and include both full-field digital mammography and synthetic 2D images derived from digital breast tomosynthesis. The proposed framework improved cross-site performance for both EMBED (AUC 0.68 to 0.72) and the Duke Calcification Dataset (AUC 0.68 to 0.73). These findings indicate that domain adaptation can reduce domain shifts and improve the generalization for calcification classification across multi-site datasets.