Research output per year
Research output per year
Personal profile
Specialisation
Dynamic deep learning applied to functional analysis of cardiac MRI
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.
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Automatic segmentation with detection of local segmentation failures in cardiac MRI
Sander, J., de Vos, B. D., Išgum, I. & Sander, J., 10 Dec 2020, In: Scientific reports. 10, 1, p. 21769 21769.Research output: Contribution to Journal › Article › Academic › peer-review
Open Access -
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Sander, J., de Vos, B. D., Wolterink, J. M. & Išgum, I., 2019, Medical Imaging 2019: Image Processing: Image Processing. Landman, B. A. & Angelini, E. D. (eds.). SPIE, Vol. 10949. 1094919. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
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Mutual information for unsupervised deep learning image registration
de Vos, B. D., van der Velden, B. H. M., Sander, J., Gilhuijs, K. G. A., Staring, M. & Išgum, I., 2020, Medical Imaging 2020: Image Processing. Isgum, I. & Landman, B. A. (eds.). SPIE, Vol. 11313. 2549729. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; vol. 11313).Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review