Outcome Prediction – Is H&E All We Need?
The rapid evolution of AI and digital pathology offers new opportunities for pathologists to be at the centre of predicting patient outcomes. By harnessing machine learning and deep learning algorithms, clinicians and researchers can identify novel biomarkers that enable more precise and personalized cancer care. This presentation will outline the major trends in digital pathology, including the development of AI-driven outcome prediction models.
Sepp De Raedt
Sepp De Raedt has extensive experience in developing state-of-the-art machine learning methods in radiology and pathology. During the DoMore! project at the Institute of Cancer Genetics and Informatics at Oslo University Hospital, he played a pivotal role in the creation of DoMore Diagnostics’ groundbreaking technology—culminating in the first fully automated method for predicting colorectal cancer patient prognosis from histopathology images and published in The Lancet. In addition to ongoing research, he leads the technical implementation of DoMore Diagnostics’ technology and bringing the technology to the patients to improve treatment and quality of life of patients.
