Hortense Le

This paper develops a self-supervised computational pathology framework to identify prognostic histomorphological phenotypes in colorectal cancer using H&E images and tile embeddings.

This paper uses self-supervised learning to identify histomorphological patterns in colon cancer WSIs, revealing clinically relevant clusters associated with survival and treatment responses.

This paper introduces a segmentation-free self-supervised AI model to analyze brain tumor microenvironments, identifying a novel tumor-associated B cell subset linked to improved survival.

The paper introduces a segmentation-free vision transformer, CANVAS, to discover and characterize tumor microenvironment signatures from high-dimensional multiplexed images, identifying a monocytic signature linked to poor prognosis.

This study identifies inflammation in tumor-adjacent lung tissue as a predictor of clinical outcome in lung adenocarcinoma using multi-omic analysis of 143 stage I cases.

This paper introduces a mobile platform called BrainE for rapid, scalable EEG-based assessment of multiple cognitive tasks and demonstrates its reliability and sensitivity to neural plasticity and mental health symptoms.