We are the research group “Clinical Artificial Intelligence”: a young, diverse, and interdisciplinary group of scientists. We use computational methods to extract actionable knowledge from clinical routine data using Deep Learning. We apply Deep Learning in Computer vision and Natural Language Processing and use it with a clinical perspective on health and disease. Our main area of expertise is precision oncology of solid tumors, including immunotherapy. We are global thought leaders in the area of predicting clinically actionable properties of tumors directly from routinely available histopathology slides. To learn more about our research, have a look at our featured publications.
Our lab is part of the Faculty of Medicine and the Faculty of Computer Science at TUD Dresden University of Technology, Dresden, Germany. In addition, our group is affilated with the Department of Medical Oncology at the National Center for Tumor Diseases Heidelberg, Heidelberg, Germany.
Since 2019, our research group has received more than 6.5 million EUR of third-party funding. We are very grateful for this and we are using these funds to push the boundaries of humanity’s knowledge in cancer research. Among others, our research is funded by the Else Kroener Fresenius Foundation, the German Cancer Aid (Projects “Max Eder research group” and “DECADE”), the German Federal Ministry of Health (Projects “DEEP LIVER”), German Federal Ministry of Education and Research (Projects “PEARL”, “SWAG”, “TRANSFORM LIVER”, “CAMINO” and “TANGERINE”), the Innovation Fund of the “Gemeinsamer Bundesausschuss” (Project “Transplant.KI”), EU Horizon (Projects “ODELIA” and “GENIAL”), the European Commission (ERC Grant “NADIR”) and the National Institute for Health and Care Research (NIHR).
The amount of routinely available data in oncology is massively increasing. Currently, we are not using this data for clinical decision making. At the same time, in data science, we are witnessing an exponential increase of state-of-the-art deep learning, especially self-supervised models, transformers and generative models. In just five years, these algorithms have massively pushed the boundary of what was technically feasible to completely new levels. However, as the fields of medicine and data science evolve faster and faster, they are becoming increasingly disconnected. Without structured efforts, it is hard to keep up to date in both fields. Our lab’s mission is to build an interdisciplinary space in which young biologists, medical doctors and computer scientists collaborate and co-develop ideas and methods for improved clinical decision making in cancer.
Planetary health is a prerequisite for individual health. For us as scientists and clinicians, it is imperative to spread awareness: we need to limit global temperature increase and restore biodiversity to protect health.