We had a wonderful week (10. DFG-#Nachwuchsakademie) with many insightful talks and fruitful discussions at the Schloss Birlinghoven! 20 participants with medical and technical backgrounds, from all over Germany, came together to learn about #AI in #Medicine!
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For …
We are also interested in developing affordable AI solutions suitable for poor-quality data generated by low infrastructure and point-of-care diagnosis.
Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, Prof. Nassir Navab, Chair for Computer Aided Medical Procedures, and Prof. Daniel Rueckert, Chair for AI in Medicine, TU Munich
Time: Fridays, 10:00 - 12:00
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and …
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks have dominated the state of the art in this task. However, since this …