Context-guided segmentation for histopathologic cancer segmentation

Abstract

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 incorporate contextual information. Our design is evaluated using the Camelyon16 dataset of sentinel lymph node tissue and cancer. The results demonstrate the benefit of including context regions when segmenting for cancer, with an improvement in AUC ranging from 0.31 to 0.92% and an improvement in cancer Dice score ranging from 4.09% to 6.81% compared to single detailed input models.