Chuhan Wang

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

This study benchmarks six methods for predicting spatial gene expression from histology images using breast and skin cancer datasets and TCGA data, evaluating their performance and translational potential.

BIDCell improves subcellular spatial transcriptomics segmentation by combining self-supervised learning with biologically informed loss functions, outperforming existing methods across multiple tissue types.