Our research focus is on data-intensive research questions in cancer research and computational pathology. We use artificial intelligence and machine learning to build computational methods and tools capable of human-level accuracy for cancer diagnostics, grading and subtyping. Our recent work has focused on developing and artificial intelligence for prostate cancer diagnostics and grading, breast cancer diagnostics, and on developing multimodal artificial intelligence for histopathology.

Digital pathology is rapidly transforming the workflow in routine diagnostics, and our goal is to enable faster, less subjective and in some cases even more accurate diagnostics through computational pathology enabled by modern machine learning. We anticipate that besides enabling decision support for tasks currently done by human experts, computational pathology has the potential for novel discoveries from histopathology beyond the limits of human vision.