About: Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs
Creator	Antani, Sameer Rajaraman, Sivaramakrishnan Alderson, Philip Folio, Les Sornapudi, Sudhir
source	MedRxiv
abstract	Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, using these models in medical computer vision tasks suffers from 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 for COVID-19 detection using chest X-rays (CXRs). Specifically, our contribution benefits from (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance compared to individual constituent models; (iii) performing statistical analyses at various learning stages to validate our claims; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We observe that: (i) ensemble approaches improved classification and localization performance; and, (ii) inter-reader variability and performance level assessment helped guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.
has issue date	2020-07-16(xsd:dateTime)
bibo:doi	10.1101/2020.07.15.20154385
has license	medrxiv
sha1sum (hex)	c2d15ed4da2dc2d3d546c29a469565d84528958f
schema:url	https://doi.org/10.1101/2020.07.15.20154385
resource representing a document's title	Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs
resource representing a document's body	covid:c2d15ed4da2dc2d3d546c29a469565d84528958f#body_text
is schema:about of	named entity 'guide' named entity 'promising' named entity 'ensembles' named entity 'Simultaneous' named entity 'demonstrate' »more»

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