Machine Learning in Biomedical Imaging

Prof. Dr. Julia Anne Schnabel

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.

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.

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About Our Research

The mission of the Institute of Machine Learning in Biomedical Imaging is 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. Julia Schnabel’s research focus is on applications in cancer, cardiovascular diseases, and maternal/perinatal health.

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Clough J, Byrne N, Öksüz I, Zimmer VA, Schnabel JA, King A

A Topological Loss Function for Deep-learning based image segmentation using persistent homology

Rueckert D, Schnabel JA

Model-based and data-driven strategies in medical image computing

Öksüz I, Clough J, Ruijsink B, Puyol Antón E, Bustin A, Cruz G, Prieto C, King AP, Schnabel JA

Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation

Öksüz, Ruijsink JB, Puyol Anton E, Clough JR, Limada Cruz, GJ, Bustin A, Prieto Vasquez C, Botnar RM, Rueckert D, Schnabel JA, King AP

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

Schnabel JA, Heinrich MP, Papież BW, Brady Sir JM

Advances and challenges in deformable image registration: From image fusion to complex motion modelling