A domain generalization framework that utilizes a foreground-only histogram matching protocol to resolve the domain shift issue arising from disparate clinical sources is proposed, significantly outperforming prominent domain generalization paradigms, including MixStyle and Discrete-Fourier-Transform-based frameworks.
Abstract
Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acquisition styles. In this work, we introduce two new datasets, BreastMammo and DenseMammo, to facilitate robust multi-view mammography research. We propose a domain generalization framework that utilizes a foreground-only histogram matching protocol to resolve the domain shift issue arising from disparate clinical sources. Internal evaluation using a 5-fold cross-validation protocol demonstrates the efficacy of our approach, with the Swin Transformer backbone achieving a peak AUC of 98.32% for density classification. External evaluation on the TNMammo and LUMINA datasets demonstrates that the proposed approach consistently reduces domain shift, significantly outperforming prominent domain generalization paradigms, including MixStyle and Discrete-Fourier-Transform-based frameworks.
Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail together with rich temporal contrast information. However, high dimensional 4D inputs, small lesions, and heterogeneous acquisition protocols across clinical sites hinder r...
Benjamin Hamm, Y. Kirchhoff, Maximilian R. Rokuss et al.· 0 citations
The study shows that transfer learning acts as a strong base for AI-assisted mammography triage systems in low-resource environments when patient level assessment continues throughout system development.
Zinat Abdulkadiri, Muhammad A. Suleiman, Joshua Abah· FUDMA Journal of Sciences· 0 citations
Breast cancer (BC) remains one of the foremost causes of cancer-related mortality among women globally, and early detection through mammography screening is essential for improving patient survival outcomes. Although recent deep learning frameworks have demonstrated encouraging performance in automated mammogram classi...
P. Revathi, B. Vigneshwaran· 2026 International Conferenc...· 0 citations
Breast cancer is a leading cause of cancer morbidity and mortality among women globally, emphasizing the need for accurate and timely diagnostic methods. A systematic but innovative two phases transfer learning based deep learning classification framework is developed using popular EfficientNetB7 architecture architect...
M. Kavya, G. Thirupati· International Journal of Sci...· 0 citations
Breast cancer accounted for approximately 2.4 million new cases and 694,000 deaths worldwide in 2024. Deep learning approaches, particularly encoder–decoder architectures, have been investigated for mammographic mass segmentation. However, evaluations are commonly centered on benchmarked datasets, which include only an...
Fabian Cienfuegos-Caraveo, A. Guzmán-Pando, G. Ramírez-Alonso et al.· Applied Sciences· 0 citations
Studies with deep learning models for mammographic breast cancer classification routinely report AUC values exceeding 0.90 on public benchmarks; however, the validity of these results is rarely examined at the protocol level. A pervasive issue is patient-level data leakage, whereby images from the same patient appear i...
