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 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 Cyc
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- PossiblePossibly related (embedding) · 48%Deep Learning-Based Oral Cancer Detection Using Clinical Images - Cureus →
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