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Dental caries detection using a semi-supervised learning approach

Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been u...

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Autores principales: Qayyum, Adnan, Tahir, Ahsen, Butt, Muhammad Atif, Luke, Alexander, Abbas, Hasan Tahir, Qadir, Junaid, Arshad, Kamran, Assaleh, Khaled, Imran, Muhammad Ali, Abbasi, Qammer H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839770/
https://www.ncbi.nlm.nih.gov/pubmed/36639724
http://dx.doi.org/10.1038/s41598-023-27808-9
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author Qayyum, Adnan
Tahir, Ahsen
Butt, Muhammad Atif
Luke, Alexander
Abbas, Hasan Tahir
Qadir, Junaid
Arshad, Kamran
Assaleh, Khaled
Imran, Muhammad Ali
Abbasi, Qammer H.
author_facet Qayyum, Adnan
Tahir, Ahsen
Butt, Muhammad Atif
Luke, Alexander
Abbas, Hasan Tahir
Qadir, Junaid
Arshad, Kamran
Assaleh, Khaled
Imran, Muhammad Ali
Abbasi, Qammer H.
author_sort Qayyum, Adnan
collection PubMed
description Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.
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spelling pubmed-98397702023-01-15 Dental caries detection using a semi-supervised learning approach Qayyum, Adnan Tahir, Ahsen Butt, Muhammad Atif Luke, Alexander Abbas, Hasan Tahir Qadir, Junaid Arshad, Kamran Assaleh, Khaled Imran, Muhammad Ali Abbasi, Qammer H. Sci Rep Article Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research. Nature Publishing Group UK 2023-01-13 /pmc/articles/PMC9839770/ /pubmed/36639724 http://dx.doi.org/10.1038/s41598-023-27808-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Qayyum, Adnan
Tahir, Ahsen
Butt, Muhammad Atif
Luke, Alexander
Abbas, Hasan Tahir
Qadir, Junaid
Arshad, Kamran
Assaleh, Khaled
Imran, Muhammad Ali
Abbasi, Qammer H.
Dental caries detection using a semi-supervised learning approach
title Dental caries detection using a semi-supervised learning approach
title_full Dental caries detection using a semi-supervised learning approach
title_fullStr Dental caries detection using a semi-supervised learning approach
title_full_unstemmed Dental caries detection using a semi-supervised learning approach
title_short Dental caries detection using a semi-supervised learning approach
title_sort dental caries detection using a semi-supervised learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9839770/
https://www.ncbi.nlm.nih.gov/pubmed/36639724
http://dx.doi.org/10.1038/s41598-023-27808-9
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