Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging

• URL: http://arxiv.org/abs/2604.17222v1
• Date: Sun, 19 Apr 2026 03:04:01 GMT
• Title: Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging
• Abstract summary: This study introduces a novel Region-Affinity Attention mechanism tailored for DUV-WSI breast cancer classification.<n>Our approach achieves an accuracy of 92.67 +/- 0.73% and an AUC of 95.97%, outperforming existing attention methods.
• Score: 3.1912286225194877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Breast cancer diagnosis demands rapid and precise tools, yet traditional histopathological methods often fall short in intra-operative settings. Deep Ultraviolet (DUV) fluorescence imaging emerges as a transformative approach, offering high-contrast, label-free visualization of whole-slide images (WSIs) with unprecedented detail, surpassing conventional hematoxylin and eosin (H&E) staining in speed and resolution. However, existing deep learning methods for breast cancer classification, predominantly patch-based, fragment spatial context and incur significant preprocessing overhead, limiting their clinical utility. Moreover, standard attention mechanisms, such as Spatial, Squeeze-and-Excitation, Global Context and Guided Context Gating, fail to fully exploit the rich, multi-scale regional relationships inherent in DUV-WSI data, often prioritizing generic feature recalibration over diagnostic specificity. This study introduces a novel Region-Affinity Attention mechanism tailored for DUV-WSI breast cancer classification, processing entire slides without patching to preserve spatial integrity. By modeling local neighbor distances and constructing a full affinity matrix, our method dynamically highlights diagnostically relevant regions, augmented by a contrastive loss to enhance feature discriminability. Evaluated on a dataset of 136 DUV-WSI samples, our approach achieves an accuracy of 92.67 +/- 0.73% and an AUC of 95.97%, outperforming existing attention methods.

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