452 citations · 761 across the 21 of their papers we have counts for
4 papers · 1 filter
Improving the repeatability of deep learning models with Monte Carlo dropout
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +7
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models outp…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
Give me (un)certainty -- An exploration of parameters that affect segmentation uncertainty
Katharina Hoebel, Ken Chang, Jay Patel +2
Segmentation tasks in medical imaging are inherently ambiguous: the boundary of a target structure is oftentimes unclear due to image quality and biological factors. As such, predi…
Temporo-Spatial Collaborative Filtering for Parameter Estimation in Noisy DCE-MRI Sequences: Application to Breast Cancer Chemotherapy Response
Xia Zhu, Dipanjan Sengupta, Andrew Beers +2
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally invasive imaging technique which can be used for characterizing tumor biology and tumor response to ra…