1 code implementation • 24 Oct 2023 • Minh Nguyen, Alan Q. Wang, Heejong Kim, Mert R. Sabuncu
CoPA assumes that (1) generation mechanism is stable, i. e. label Y and confounding variable(s) Z generate X, and (2) the unstable conditional prevalence in each site E fully accounts for the unstable correlations between X and Y .
no code implementations • 2 Oct 2023 • Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, Mert R. Sabuncu
To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
no code implementations • 6 Jul 2023 • Heejong Kim, Victor Ion Butoi, Adrian V. Dalca, Daniel J. A. Margolis, Mert R. Sabuncu
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models.
1 code implementation • 5 Apr 2023 • Heejong Kim, Mert R. Sabuncu
For example, normalizing for nuisance variation might be hard in settings where there are a lot of idiosyncratic changes.
1 code implementation • 24 Jun 2021 • Mengwei Ren, Heejong Kim, Neel Dey, Guido Gerig
Further, such approaches can restrict downstream usage of variably sampled DWIs for usages including the estimation of microstructural indices or tractography.
1 code implementation • 17 Feb 2021 • Axel Elaldi, Neel Dey, Heejong Kim, Guido Gerig
We then show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset.