Marvin Lerousseau

sCellST predicts single-cell gene expression from H&E images using spatial transcriptomics data, outperforming traditional methods in pancreatic cancer and detecting morphological differences in ovarian cancer.

This paper introduces Giga-SSL, a self-supervised method for learning Whole Slide Image representations from The Cancer Genome Atlas data, improving classification performance and enabling efficient analysis.