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AI framework improves mammography calcification classification across datasets

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel framework for calcification classification in mammography, aiming to improve diagnostic accuracy across different datasets and imaging techniques. The system utilizes unsupervised domain adaptation with style transfer models like AdaIN and CycleGAN to generate diverse training samples without requiring additional annotations. A Swin Transformer V2 backbone then performs the supervised classification. This approach demonstrated improved performance on external validation datasets, increasing AUC scores for EMBED and the Duke Calcification Dataset, thereby reducing domain shift issues and enhancing generalization. AI

IMPACT Enhances generalization of AI models in medical imaging, potentially improving diagnostic accuracy for breast cancer detection.

RANK_REASON The cluster contains an academic paper detailing a new method for medical image analysis.

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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