Tristan Lazard

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.

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.

The paper proposes a modified cross-validation approach and a new neural network architecture for treatment response prediction in small Triple Negative Breast Cancer cohorts.