Please see my Research Gate or Google Scholar for the full list!

Preprint: accepted at ICCV as Oral (top ~4%), * equal contributions, 2019.

Preprint: under review at Neuroimage, 2019.

In ICML, Oral, 2019.

In CVPR, 2019.

In MICCAI, 2018.

In MICCAI, 2018.

In MICCAI, Spotlight (top ~5%), 2018.

In ICML, Long Oral, 2018.

In MICCAI, Oral (top ~4%) and Winner of Young Scientist Award, 2017.

In NeuroImage, 2017.

We propose a Bayesian variant of random forests, which provides an estimate of uncertainty over prediction which can be used to assess its accuracy in the absence of ground truth. We have shown that the predictive uncertainly correlates well with the accuracy and can highlight abnormality not represented in the training data.
In MICCAI, 2016.

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