Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

ABSTRACT: Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection usin

SUBMITTER: Rajaraman S 

PROVIDER: S-EPMC7660555 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature