2 Million Euros in Funding for Prof. Dr. Bernhard Kainz!
We are proud to announce that the European Research Council (ERC) awarded a Consolidator Grant to our Prof. Bernhard Kainz – the most prestigious research funding award in Europe! The grant is 2 million Euros and will be used over a period of five years to support his research. Prof. Kainz has been working at FAU at the Department Artificial Intelligence in Biomedical Engineering since September 2021 and is Professor for Image Data Exploration and Analysis.
The project for which he has received the grant works on recognizing healthy tissue structures with Artificial Intelligence. Imaging is becoming more and more important in medicine, but analyzing images is both time-consuming and costly. It strains medical personnel, makes the procedures more expensive and increases waiting times for patients. This is where the project, which focuses on automated medical image analysis, comes into play: Computer tools based on artificial intelligence could reliably recognize healthy human tissue based on image material and thus ease the workload for experts working in the field. Artificial intelligence would then be able to pre-sort the images obtained during the diagnostic process into “probably healthy” or “possibly sick”. Of course, the final decision is made by medical experts. This improved procedure saves valuable time, which the medical staff can use to analyze unusual images, and overall decreases waiting periods for patients. The computer tools will be trained to recognize normal physiological traits and any unusual changes over a certain period of time in individual patients. In addition, they could match patient information provided by physicians (for example laboratory results) to the available images.
But what drives Kainz? “I am convinced that everyone deserves the same quality of medical care, no matter where they live or how much they earn. This is why our team is working on developing methods that make high-quality medical imaging analyses widely available and scalable.”
The reason why AI is trained with images of healthy tissue instead of abnormal tissue is simple. “Training machine learning tools using hundreds and hundreds of examples of every possible disease would be extremely costly in terms of time and manpower. Medical experts who are already overstretched would have to provide and comment on vast amounts of images of pathological structures.” In his opinion, this is why it makes much more sense to “feed” the AI with images of healthy tissue structures. This strategy is time-consuming enough, as healthy tissue differs depending on age and other characteristics such as gender. The research group around Prof. Dr. Bernhard Kainz will work on solutions for this and other challenges in the next years. Their work could significantly improve the imaging diagnostic process in the future.
Students of Medical Engineering can take lectures and seminars with Prof. Dr. Bernhard Kainz and write their thesis with him. If you are interested in having Prof. Dr. Bernhard Kainz as your supervisor for your thesis, you should look into his past research and then contact him via e-mail (email@example.com).