134 citations · 431 across the 17 of their papers we have counts for
5 papers · 1 filter
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.…
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…
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…