48 citations · 95 across the 21 of their papers we have counts for
3 papers · 1 filter
Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images
Tareen Dawood, Chen Chen, Baldeep S. Sidhua +9
Quantifying uncertainty of predictions has been identified as one way to develop more trustworthy artificial intelligence (AI) models beyond conventional reporting of performance m…
Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views
Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran +2
Abnormal spleen enlargement (splenomegaly) is regarded as a clinical indicator for a range of conditions, including liver disease, cancer and blood diseases. While spleen length me…
Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training
Tareen Dawood, Emily Chan, Reza Razavi +2
In terms of accuracy, deep learning (DL) models have had considerable success in classification problems for medical imaging applications. However, it is well-known that the output…