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.

翻译：乳腺癌诊断需要快速且精准的工具，但传统组织病理学方法在术中场景中往往存在不足。深紫外荧光成像作为一种变革性技术，能以高对比度、无标记方式实现全切片图像的可视化，其细节水平超越传统苏木精-伊红染色，并在速度和分辨率上具有显著优势。然而，现有基于深度学习（主要采用分块策略）的乳腺癌分类方法会破坏空间上下文信息，且预处理开销巨大，限制了临床实用性。此外，标准注意力机制（如空间注意力、压缩激励注意力、全局上下文注意力和引导式上下文门控注意力）未能充分挖掘深紫外全切片图像数据中固有的丰富多尺度区域关联，通常优先进行通用特征重校准而非诊断特异性增强。本研究提出一种专用于深紫外全切片图像乳腺癌分类的新型区域亲和力注意力机制，无需分块即可处理全切片图像，从而保持空间完整性。该方法通过建模局部邻域距离并构建完整亲和度矩阵，动态突出诊断相关区域，并辅以对比损失函数增强特征判别能力。在包含136例深紫外全切片图像样本的数据集上，本方法实现了92.67%±0.73%的准确率和95.97%的AUC值，性能优于现有注意力方法。