Our paper “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium? A Feasibility Study” was among the top 20 most-cited papers of Investigative Radiology in 2021. Investigative Radiology is the second highest ranked journal in the field of radiology with a current impact factor of 10.065.
From Code to Clinic, Smart Hospital Tech Boosts Efficiency, Sustainability in Medicine. NVIDIA’s Blog features our advances in smart hospital platforms, improving patient care and reducing energy consumption.
The research group Medical Machine Learning works on developing and deploying cutting-edge machine learning methods with the goal of making a meaningful difference for patients, doctors, and hospital staff. To this end, and in collaboration with a digital-forward clinic administration, it continues to build on a SMART hospital information technoloy structure that provides access to real-world medical data. Strong funding and state-of-the-art equipment support this effort. One focus of the group lies in the exploration of unsupervised learning paradigms for recognition of oncologically relevant patterns in large and complex data.
A common approach to medical research is called “from bench to bedside”: to use insights gained in laboratory experiments to inform new ways of treating patients. In an analogous approach - “from bits to bedside” - we aim to bring our algorithms to the point-of-care, i.e., to translate them into clinical practice.
We are part of the Cancer Research Center Cologne Essen (CCCE). In addition, Jens Kleesiek is PI in the German Cancer Consortium (DKTK) and at the Helmholtz Information & Data Science School for Health. There is a close cooperation with the German Cancer Center (DKFZ) for developing the Joint Imaging Platform (JIP) for distributed data analysis and federated learning.