Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning.

ABSTRACT: Accurate detection, localization, and staging of breast cancer lymph node metastases are critical for guiding treatment decisions and predicting patient outcomes. This study presents a selective neighborhood attention-based deep learning framework that combines nuclei-level features with high-level tissue embeddings to detect, annotate and stage breast cancer metastases in whole-slide images (WSIs) of lymph node biopsy specimens precisely. The proposed framework leverages a dual-path feature extractor, incorporating both nuclei segmentation/classification outputs and transformer-based tissue features, alongside a dynamic attention mechanism that selects and emphasizes neighboring patches based on similarity to the target patch. Experimental results on the CAMELYON16 test set demonstrate hi

SUBMITTER: Tauqeer A 

PROVIDER: S-EPMC12572172 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature