Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions

ArXi:2604.24347v1 Announce Type: cross In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are readily available than fine-grained annotations. However, these assessments are rarely mapped to pixel-wise segmentation. The task is fundamentally underdetermined, as many spatially distinct segmentations can satisfy the same global proportions in the absence of pixel-wise constraints. To address this, we