Raphaël Bourgade

HEDeST improves spatial transcriptomic deconvolution by integrating histology features, achieving single-cell resolution and outperforming existing methods in simulated and real cancer datasets.

This paper develops a deep learning model that accurately segments tumors using only eight whole-slide images, showing that small, well-annotated datasets can achieve high performance in medical imaging.