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One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features

Ter-Sarkisov, A. ORCID: 0000-0002-1300-6132 (2021). One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features. Applied Soft Computing,

Abstract

We present a novel framework that integrates segmentation of lesion masks and prediction of COVID-19 in chest CT scans in one shot. In order to classify the whole input image, we introduce a type of associations among lesion mask features extracted from the scan slice that we refer to as affinities. First, we map mask features to the affinity space by training an affinity matrix. Next, we map them back into the feature space through a trainable affinity vector. Finally, this feature representation is used for the classification of the whole input scan slice.

We achieve a 93.55% COVID-19 sensitivity, 96.93% common pneumonia sensitivity, 99.37% true negative rate and 97.37% F1-score on the test split of CNCB-NCOV dataset with 21192 chest CT scan slices. We also achieve a 0.4240 mean average precision on the lesion segmentation task. All source code, models and results are publicly available on https://github.com/AlexTS1980/COVID-Affinity-Model.

Publication Type: Article
Additional Information: © <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: COVID-19, Computer Vision, Object detection, Object segmentation, Image classification
Subjects: H Social Sciences > HN Social history and conditions. Social problems. Social reform
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QR Microbiology
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
R Medicine > RC Internal medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date available in CRO: 10 Dec 2021 09:35
Date deposited: 10 December 2021
Date of acceptance: 4 December 2021
URI: https://openaccess.city.ac.uk/id/eprint/27226
[img] Text - Accepted Version
This document is not freely accessible due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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