1.5k citations · 1.9k across the 16 of their papers we have counts for
16 papers
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…
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling
Sourav Kumar, A. Lakshminarayanan, Ken Chang +5
Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not…
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Charles Lu, Syed Rakin Ahmed, Praveer Singh +1
Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical dep…
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.…
Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations
Aaron S. Coyner, Praveer Singh, James M. Brown +5
Background: Artificial intelligence (AI) may demonstrate racial bias when skin or choroidal pigmentation is present in medical images. Recent studies have shown that convolutional…
Deploying clinical machine learning? Consider the following...
Charles Lu, Ken Chang, Praveer Singh +5
Despite the intense attention and considerable investment into clinical machine learning research, relatively few applications have been deployed at a large-scale in a real-world c…
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging
Miao Zhang, Liangqiong Qu, Praveer Singh +2
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different instit…
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…
Towards Trainable Saliency Maps in Medical Imaging
Mehak Aggarwal, Nishanth Arun, Sharut Gupta +9
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread…
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…
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…
Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris, Praveer Singh, Nikos Komodakis
Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic…