17 citations · 36 across the 7 of their papers we have counts for
5 papers · 1 filter
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology
Tiago Gonçalves, Dagoberto Pulido-Arias, Julian Willett +8
The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date,…
A generalized framework to predict continuous scores from medical ordinal labels
Katharina V. Hoebel, Andreanne Lemay, John Peter Campbell +7
Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to…
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…