Dental caries detection using a semi-supervised learning approach.

Autor: Qayyum A; James Watt School of Engineering, University of Glasgow, Glasgow, UK.; Information Technology University of the Punjab, Lahore, Pakistan., Tahir A; James Watt School of Engineering, University of Glasgow, Glasgow, UK.; Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan., Butt MA; Information Technology University of the Punjab, Lahore, Pakistan., Luke A; Department of Clinical Sciences, College of Dentistry, Ajman University, Ajman, UAE.; Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE., Abbas HT; James Watt School of Engineering, University of Glasgow, Glasgow, UK., Qadir J; Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar., Arshad K; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE., Assaleh K; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE., Imran MA; James Watt School of Engineering, University of Glasgow, Glasgow, UK.; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE., Abbasi QH; James Watt School of Engineering, University of Glasgow, Glasgow, UK. qammer.abbasi@glasgow.ac.uk.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Jan 13; Vol. 13 (1), pp. 749. Date of Electronic Publication: 2023 Jan 13.
DOI: 10.1038/s41598-023-27808-9
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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