Improving the data efficiency of machine (deep) learning methods through multi-modal self-supervised (unsupervised) algorithms. Aiming at significantly reducing the human annotation required for medical application
Multimodal Self-Supervised Learning for Medical Image Analysis: we propose a novel self-supervised multimodal puzzle-solving proxy task, which facilitates neural network representation learning from multiple image modalities.
3D Self-Supervised Methods for Medical Imaging: we propose five different methods, which facilitate feature learning from unlabeled 3D scans.
Strong multimodal alignment of image and text patient data samples from a large real-world clinical corpus with a variety of cases. Aiming at improving disease detection, disease reporting, and annotation efficiency.
Prof. Dr. Christoph Lippert Professor for Digital Health & Machine Learning Room: G-2.1.xx Tel.: +49-(0)331 5509-4850 E-Mail: christoph.lippert(at)hpi.de
Campus III, Haus G2 Room: G-2.1.22 Tel.: +49-(0)331 5509-4850 Fax: +49-(0)331 5509-4849 E-Mail: office-lippert(at)hpi.de
Campus III Building G2 Rudolf-Breitscheid-Straße 187 14482 Potsdam, Germany