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AI framework improves mammography calcification classification across datasets

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework to improve the accuracy of AI models in classifying calcifications in mammography across different datasets and imaging techniques. This framework utilizes unsupervised domain adaptation, employing style transfer models like AdaIN and CycleGAN to generate diverse training data without requiring additional annotations. A Swin Transformer V2 backbone then performs the classification. The method demonstrated improved performance on external datasets, suggesting its potential to reduce domain shifts and enhance the generalization of AI diagnostic tools in mammography. AI

IMPACT Enhances AI diagnostic capabilities in mammography by improving generalization across diverse datasets and imaging techniques.

RANK_REASON The cluster contains a research paper detailing a novel AI framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI framework improves mammography calcification classification across datasets

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xuan Liu, Derek L. Nguyen, Emily C. Barre, Jennifer Thomas, Thomas Lynch, Jeffrey R. Marks, E. Shelley Hwang, Marc D. Ryser, Joseph Y. Lo, Lars J. Grimm ·

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

    arXiv:2607.06549v1 Announce Type: new Abstract: 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 ch…

  2. arXiv cs.CV TIER_1 English(EN) · Lars J. Grimm ·

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

    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 u…

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