Context-guided segmentation for histopathologic cancer segmentation.

ABSTRACT: Microscopic inspection of histologically stained tissue is considered as the gold standard for cancer diagnosis. This research is inspired by the practices of pathologists who analyze diagnostic samples by zooming in and out. We propose a dual-encoder model that simultaneously evaluates two views of the tissue at different levels of magnification. The lower magnification view provides contextual information for a target area, while the higher magnification view provides detailed information. The model consists of two encoder branches that consider both detail and context resolutions of the target area concurrently for binary pixel-wise segmentation. We introduce a unique weight initialization for the cross-attention between the context and detail feature tensors, allowing the model to inco

SUBMITTER: Juybari J 

PROVIDER: S-EPMC11825859 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature