Unsupervised few shot learning architecture for diagnosis of periodontal disease in dental panoramic radiographs.

Autor: Kim MJ; Department of Medical and Digital Engineering, Hanyang University, Seoul, 04763, Republic of Korea., Chae SG; Department of Industrial Engineering, Hanyang University, Seoul, 04763, Republic of Korea., Bae SJ; Department of Industrial Engineering, Hanyang University, Seoul, 04763, Republic of Korea. sjbae@hanyang.ac.kr., Hwang KG; Department of Dentistry, College of Medicine, Hanyang University, Seoul, 04763, Republic of Korea. hkg@hanyang.ac.kr.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 05; Vol. 14 (1), pp. 23237. Date of Electronic Publication: 2024 Oct 05.
DOI: 10.1038/s41598-024-73665-5
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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