Prof. Dr. Julia Anne Schnabel
Institute of Machine Learning in Biomedical Imaging
Julia Schnabel's Institute focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to fundamentally transform the use of imaging for diagnostics and prognostics. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome.
The Institute of Machine Learning in Biomedical Imaging of Julia Schnabel focuses on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Its goal is to fundamentally transform the use of imaging for diagnostics and prognostics. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome.
Publications
Kiechle, J. ; Fischer, S.M. ; Lang, D. ; Bercea, C.-I. ; Nyflot, M.J. ; Felsner, L. ; Schnabel, J.A. ; Peeken, J.C.
TomoGraphView: 3D medical image classification with omnidirectional slice representations and graph neural networks.Sens, D. ; Shilova, L. ; Dalca, A.V. ; Schnabel, J.A. ; Casale, F.P.
GEMCONT:Genetics-based Multimodal Contrastive Learning Enhances Phenotypic embeddings and Boosts Genetic Discovery.Fischer, S.M. ; Kiechle, J. ; Daza, L. ; Felsner, L. ; Osuala, R. ; Lang, D. ; Lekadir, K. ; Peeken, J.C. ; Schnabel, J.A.
Progressive growing of patch size: Curriculum learning for accelerated and improved medical image segmentation.
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