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
Reuter, L. ; Kraus, K.M. ; Fischer, S.M. ; Pletzer, D. ; Bernhardt, D. ; Combs, S.E. ; Schnabel, J.A. ; Peeken, J.C.
Prediction of symptomatic radiation pneumonitis in lung cancer patients: A radiomics and dosiomics machine learning approach using the prospective multicenter RTOG 0617 and REQUITE trials.Konz, N. ; Osuala, R. ; Verma, P. ; Chen, Y. ; Gu, H. ; Dong, H. ; Chen, Y. ; Marshall, A. ; Garrucho, L. ; Kushibar, K. ; Lang, D. ; Kim, G.S. ; Grimm, L.J. ; Lewin, J.M. ; Duncan, J.S. ; Schnabel, J.A. ; Diaz, O. ; Lekadir, K. ; Mazurowski, M.A.
Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Eichhorn, H. ; Spieker, V. ; Hammernik, K. ; Saks, E. ; Felsner, L. ; Weiss, K. ; Preibisch, C. ; Schnabel, J.A.
Motion-robust T∗2 quantification from low-resolution gradient echo brain MRI with physics-informed deep learning.
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