134 citations · 231 across the 12 of their papers we have counts for
13 papers
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…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Monte Carlo dropout increases model repeatability
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +5
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Among the main features of robustness is repeatability. Much attention is gi…
Fair Conformal Predictors for Applications in Medical Imaging
Charles Lu, Andreanne Lemay, Ken Chang +2
Deep learning has the potential to automate many clinically useful tasks in medical imaging. However translation of deep learning into clinical practice has been hindered by issues…
Evaluating subgroup disparity using epistemic uncertainty in mammography
Charles Lu, Andreanne Lemay, Katharina Hoebel +1
As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively…
Addressing catastrophic forgetting for medical domain expansion
Sharut Gupta, Praveer Singh, Ken Chang +13
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions
Sharut Gupta, Praveer Singh, Ken Chang +9
Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institutio…
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…
Assessing the validity of saliency maps for abnormality localization in medical imaging
Nishanth Thumbavanam Arun, Nathan Gaw, Praveer Singh +5
Saliency maps have become a widely used method to assess which areas of the input image are most pertinent to the prediction of a trained neural network. However, in the context of…
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…
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Andrew Beers, James Brown, Ken Chang +4
Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a deep learning…