Knowledge-Guided Semantic Confusion-Aware and Reliable Spatial Gradient Learning for Cell Abundance Prediction from H&E Images

Accurate prediction of the abundance and spatial distribution of different cell types within tissues is essential for characterizing tissue heterogeneity and disease microenvironments. Existing two-stage methods are susceptible to error propagation from intermediate gene expression prediction, whereas one-stage methods that directly predict cell abundance from H&E images still suffer from insufficient modeling of cell-type confusion and inadequate preservation of local spatial structures. To address these issues, we propose a Knowledge-Guided Semantic Confusion-Aware and Reliable Spatial Gradient Learning framework for cell abundance prediction from H&E images. Specifically, we develop a knowledge-guided cell semantic confusion-aware relative abundance learning mechanism to reduce abundance confusion among biologically related cell types. We further propose a reliable region-aware spatial gradient consistency constraint to improve the spatial consistency of local cell abundance variations. Experiments on multiple public spatial transcriptomics datasets demonstrate that the proposed method effectively improves fine-grained cell abundance prediction and enhances the recovery of spatial cell distributions.