1.5k citations · 1.9k across the 16 of their papers we have counts for
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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…
Three Applications of Conformal Prediction for Rating Breast Density in Mammography
Charles Lu, Ken Chang, Praveer Singh +1
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinicall…
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.…
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…
Deep Tone Mapping Operator for High Dynamic Range Images
Aakanksha Rana, Praveer Singh, Giuseppe Valenzise +3
A computationally fast tone mapping operator (TMO) that can quickly adapt to a wide spectrum of high dynamic range (HDR) content is quintessential for visualization on varied low d…