Wilma E. Mesker

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 study investigates how CAFs drive cancer cell invasion through matrix remodeling and inter-spheroid proximity in a 3D in vitro model of Luminal A breast cancer.

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

The paper uses whole-slide ECM imaging to show dense fibrous matrix is linked to higher recurrence risk in Stage II colon cancer.