Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs.
Artificial intelligence
Clustering algorithm
Convolutional variational autoencoder (CVAE)
Deep learning
UNet
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Oct 2024
05 Oct 2024
Historique:
received:
14
03
2024
accepted:
19
09
2024
medline:
6
10
2024
pubmed:
6
10
2024
entrez:
5
10
2024
Statut:
epublish
Résumé
In the domain of medical imaging, the advent of deep learning has marked a significant progression, particularly in the nuanced area of periodontal disease diagnosis. This study specifically targets the prevalent issue of scarce labeled data in medical imaging. We introduce a novel unsupervised few-shot learning algorithm, meticulously crafted for classifying periodontal diseases using a limited collection of dental panoramic radiographs. Our method leverages UNet architecture for generating regions of interest (RoI) from radiographs, which are then processed through a Convolutional Variational Autoencoder (CVAE). This approach is pivotal in extracting critical latent features, subsequently clustered using an advanced algorithm. This clustering is key in our methodology, enabling the assignment of labels to images indicative of periodontal diseases, thus circumventing the challenges posed by limited datasets. Our validation process, involving a comparative analysis with traditional supervised learning and standard autoencoder-based clustering, demonstrates a marked improvement in both diagnostic accuracy and efficiency. For three real-world validation datasets, our UNet-CVAE architecture achieved up to average 14% higher accuracy compared to state-of-the-art supervised models including the vision transformer model when trained with 100 labeled images. This study not only highlights the capability of unsupervised learning in overcoming data limitations but also sets a new benchmark for diagnostic methodologies in medical AI, potentially transforming practices in data-constrained scenarios.
Identifiants
pubmed: 39369017
doi: 10.1038/s41598-024-73665-5
pii: 10.1038/s41598-024-73665-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23237Subventions
Organisme : Ministry of Trade, Industry and Energy
ID : 20204010600090
Organisme : Ministry of Health and Welfare
ID : HI20C0013
Informations de copyright
© 2024. The Author(s).
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