Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging
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-Exci...
| Authors | Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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