T. Lazard

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

This paper develops a self-supervised learning model to predict iCCA transcriptomic classes from histological images, achieving good to very good performance in multiple validation sets.

Automatic grading of cervical biopsies by combining full and self-supervision

This paper combines full and self-supervision to improve cervical biopsy grading, achieving high accuracy and interpretability.

Deep Learning identifies new morphological patterns of Homologous Recombination Deficiency in luminal breast cancers from whole slide images.

This study uses deep learning to identify new morphological patterns of HRD in luminal breast cancers from whole slide images, achieving high accuracy and enabling new hypotheses on phenotypic impacts.