Research interests

We develop a framework for dynamic deep learning. This system can learn continuously from feedback from experts. It can learn easy concepts first and gradually learn complex tasks after having seen more data. The network will express its uncertainty and ask for feedback on cases it is uncertain about or has not seen before.

specialisation

Dynamic deep learning applied to functional analysis of cardiac MRI

Research output

  1. Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI

    Research output: Contribution to journal › Article › Academic › peer-review

  2. Towards automatic classification of cardiovascular magnetic resonance Task Force Criteria for diagnosis of arrhythmogenic right ventricular cardiomyopathy

    Research output: Contribution to journal › Article › Academic › peer-review

  3. Automatic segmentation with detection of local segmentation failures in cardiac MRI

    Research output: Contribution to journal › Article › Academic › peer-review

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